Electronic function test system of high-voltage PTC (Positive Temperature Coefficient) electric heater

Abstract

The invention discloses an electronic function test system of a high-voltage PTC (Positive Temperature Coefficient) electric heater, which relates to the technical field of PTC electric heaters and is characterized in that extreme temperature, humidity and voltage disturbance data are acquired in a programmable multi-dimensional environment simulation cabin, and the temperature deviation of the high-voltage PTC electric heater is predicted by using a multi-channel fusion and compensation model; according to the method, the high-voltage PTC electric heater is used as a power source, the output power is adjusted in a real-time feedforward and feedback mode in the self-adaptive controller, finally, through dynamic calibration and closed-loop verification, a model and gain parameters are optimized in multiple scenes, a complete system which can deal with large environmental changes and is high in temperature control precision is formed, the stability and safety of the high-voltage PTC electric heater under the severe working condition are remarkably improved, and the high-voltage PTC electric heater is suitable for being used in the field of electric heaters. And overshoot and lag are reduced, the energy utilization efficiency is improved, the system is suitable for industrial heating, vehicle warm air and other scenes needing high reliability and quick response, and the maintenance cost can be effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of PTC electric heaters, in particular to an electronic function testing system for high-voltage PTC electric heaters. Background Art

[0002] High-voltage PTC (positive temperature coefficient) electric heaters are widely used in modern industrial production and vehicle heating systems due to their fast heating, high thermal efficiency, and self-limiting current. However, these heaters often face even more severe performance challenges in extremely variable external environments or complex operating conditions. For example, during the cold winter startup phase in cold regions, ambient temperatures can plummet to tens of degrees Celsius below zero, accompanied by high humidity or snow cover. Similarly, in remote construction or areas with temporary power grids, grid voltages often fluctuate dramatically over a wide range, even experiencing momentary power outages or overvoltages. Under these multiple environmental interferences, traditional constant indoor environment testing methods fail to accurately reflect actual operating conditions. This leads to risks such as temperature overshoot, hysteresis, and failure to achieve the expected heating effect after field deployment of PTC electric heaters. To ensure the stability and safety of these critical components in extreme environments, a test platform with programmable temperature, humidity, and voltage simulation capabilities is required during design and verification. Multi-channel sensor networks are also required to collect detailed data to accurately evaluate their operating characteristics under these harsh conditions.

[0003] However, existing industrial test systems generally lack the ability to globally simulate and dynamically control extreme external environments, making it difficult to systematically monitor and calibrate the temperature control performance of PTC heaters in real-world scenarios such as rapid temperature changes, high humidity shocks, and large grid voltage fluctuations. In particular, when ambient temperature, humidity, and supply voltage fluctuate significantly over a short period of time, traditional temperature control strategies using fixed parameters or simple PID control often fail to promptly sense and quantify the impact of these external factors on the PTC element's resistance characteristics. This can cause actual heating power to deviate significantly from the target temperature, leading to slow power response, temperature overshoot, or undershoot. This not only reduces energy efficiency but also poses a safety hazard to the equipment. Therefore, a comprehensive technical solution is urgently needed that can achieve multi-channel data fusion and adaptive temperature control in non-constant environments. This solution would provide extreme condition testing through a programmable simulation chamber, accurately estimate temperature deviations using an environmental compensation model, and dynamically adjust heater power using a closed-loop adaptive control method. This approach ensures that high-voltage PTC heaters maintain stable, accurate, and efficient temperature output under various complex operating conditions.

[0004] To this end, the present invention provides an electronic function testing system for a high-voltage PTC electric heater. Summary of the Invention

[0005] (1) Technical problems solved

[0006] In response to the shortcomings of the existing technology, the present invention provides an electronic function test system for high-voltage PTC electric heaters. By collecting extreme temperature, humidity and voltage disturbance data in a programmable multi-dimensional environmental simulation chamber, the temperature deviation of the high-voltage PTC electric heater is predicted using a multi-channel fusion and compensation model, and the output power is adjusted in real-time feedforward and feedback in an adaptive controller. Finally, through dynamic calibration and closed-loop verification, the model and gain parameters are optimized in multiple scenarios to form a complete system that can cope with large environmental changes and has high temperature control accuracy, significantly improving the stability and safety of the high-voltage PTC electric heater under harsh working conditions; thereby solving the technical solution recorded in the background technology.

[0007] (2) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: an electronic function test system for a high-voltage PTC electric heater, comprising: when it is detected that the high-voltage PTC electric heater needs to simulate an extreme environment, a multi-dimensional environmental simulation chamber performs programmable temperature and humidity control and variable voltage source operation, periodically adjusts environmental parameters and collects them in real time, and generates multi-source basic data containing extreme disturbances;

[0009] After the data is aggregated, wavelet filtering and feature extraction algorithms are used on the environmental parameter data to generate denoised and normalized signals and to construct an environmental compensation model to output the environmental disturbance compensation amount;

[0010] When the environmental compensation model outputs the environmental disturbance compensation in real time and detects that the temperature deviation is greater than expected, the adaptive temperature control algorithm combines the feedforward and feedback paths to superimpose the corresponding disturbance information in the online adjustment of the PID gain to form a corrected power control signal;

[0011] When new environmental disturbance data appears, multi-scenario tests are performed in the environmental simulation chamber and the temperature response curve and environmental disturbance compensation amount are recorded. The model weights and control gain parameters are optimized synchronously based on the cumulative temperature deviation evaluation results.

[0012] Furthermore, programmable heat and cold sources, adjustable humidity generators, and variable voltage sources are installed inside the simulation cabin. Composite materials that are resistant to temperature difference shocks are selected, and thermal insulation and isolation layers are laid out inside, along with an additional air duct system.

[0013] A sensor array is constructed in the simulation cabin according to high temperature areas, low temperature areas, and strong convection areas to collect environmental parameter components and then construct an environmental comprehensive value. When the environmental comprehensive value rises rapidly, an alert is issued to the outside.

[0014] Furthermore, all original signals are first time-aligned, and an adaptive filtering strategy based on wavelet transform is used to decompose each channel signal at different scales; after filtering in different frequency bands, the signals are reconstructed, and the key features are extracted and recorded as multi-channel feature vectors to obtain multi-channel signals.

[0015] Furthermore, a high-dimensional input vector is formed by integrating the multi-channel signals, the components of the multi-channel feature vectors and the comprehensive value of the environment;

[0016] An environmental compensation model is constructed based on a high-dimensional input vector to output the environmental disturbance compensation for the temperature deviation. An online learning method is used to iteratively update the weight vector of the environmental compensation model.

[0017] Furthermore, the control signal of the adaptive temperature controller is divided into a feedforward compensation component and a feedback component. The environmental disturbance compensation amount passes through a nonlinear mapping operator and is multiplied by a gain coefficient to obtain a feedforward signal.

[0018] The conventional PID or incremental PID controller is transformed into an adaptive PID controller so that its gain coefficient can be gradually corrected during operation, and the environmental disturbance compensation amount is included in the gain update rule.

[0019] Furthermore, a feedforward compensation component and a feedback component are calculated according to a feedforward algorithm and a feedback algorithm to obtain a total output, and the total output is converted into an actual heater power or a duty cycle adjustment signal to control the output heat of the high-voltage PTC electric heater;

[0020] The environmental disturbance compensation is updated according to the latest environmental data or model prediction, and the adaptive PID controller then combines the current error and the environmental disturbance compensation to complete the adaptive adjustment of the gain.

[0021] Furthermore, after configuring several sets of extreme test scenarios, the environmental parameters and control signals are recorded synchronously, and the temperature response curves, temperature control signals and various environmental parameter variables under several sets of scenarios are output;

[0022] Based on the environmental compensation model and its prediction effect on the environmental disturbance compensation amount, a cumulative temperature deviation functional is constructed;

[0023] If the accumulated temperature deviation functional is higher than expected, quadratic regression or online learning update is performed using the feedback data to correct the weight vector or nonlinear mapping function of the environmental compensation model.

[0024] Furthermore, for the adaptive PID controller, the initial gain is reset or the gain update rule is adjusted according to the overshoot and adjustment time existing in the test results;

[0025] If the modification of the environmental compensation model causes a significant change in the amount of environmental disturbance compensation, the gain coefficient of the feedforward compensation or the parameters of the nonlinear function need to be updated synchronously to obtain the adjusted adaptive temperature control strategy.

[0026] Furthermore, for the adjusted environmental compensation model-PID controller combination, a convergence index for evaluating the gradual convergence of the self-learning algorithm is defined. If the convergence index continues to oscillate or rise during long-term testing, the self-learning convergence index is modified.

[0027] Furthermore, the temperature response curve, cumulative temperature deviation functional and convergence index collected during the long-term test are visualized and comprehensively analyzed to determine whether the expected industrial application indicators have been achieved; if the long-term verification shows that the industrial application indicators do not meet expectations, further iterative optimization can be carried out based on the verification results.

[0028] (3) Beneficial effects

[0029] The present invention provides an electronic function test system for a high-voltage PTC electric heater, which has the following beneficial effects:

[0030] Build a multi-dimensional environmental simulation cabin (including programmable temperature source, humidity source and voltage source) and deploy a precision sensor network to control the temperature T in the cabin. env (t), relative humidity in the cabin H env (t), power supply voltage V env (t) and heater current I load (t) Achieve fine-grained data collection on multiple channels, enabling the acquisition of real extreme environment data at an early stage and the establishment of rich test scenarios;

[0031] Through multi-channel data cleaning and feature extraction, combined with historical operation records, an environmental compensation model is constructed to output the environmental disturbance compensation amount ΔT pred (t), using the denoised signal and the multi-channel feature vector F(t) obtained from wavelet decomposition and high-dimensional regression, an accurate prediction of the resistance change and temperature deviation trend of the PTC component is achieved. Compared with conventional linear methods, this compensation model can maintain higher accuracy in dynamic and complex external interference due to the integration of nonlinear mapping and environmental disturbance information;

[0032] The environmental disturbance compensation ΔT is calculated by the adaptive temperature control algorithm. pred (t) is combined with the conventional feedback link to achieve bidirectional control of feedforward and feedback, allowing the control signal u(t) to correct the power output in advance before the sudden change in the environment has a full impact. By implementing this strategy online, the incremental PID or adaptive PID can automatically correct the gain in different scenarios to avoid temperature overshoot and long lag. Compared with the traditional constant parameter PID, this solution can maintain fast and stable temperature control under conditions such as large external voltage fluctuations or extreme cold and high humidity.

[0033] By setting multiple sets of extreme environmental scenarios, the actual temperature T meas (t) and environmental disturbance compensation ΔT pred (t) and control signal u(t); then the model-controller is collaboratively tuned; long-term operation and self-learning convergence tests are performed, and the overall operating quality is judged in combination with the cumulative temperature deviation functional γ. This process can effectively discover potential defects of the system under extreme conditions, and continuously improve temperature control accuracy and stability through iterative calibration, achieving high reliability and high performance under harsh operating conditions;

[0034] In summary, this solution, through the close coordination and data closed loop of the four major modules of the environmental simulation chamber, multi-channel data processing, adaptive temperature control algorithm, and dynamic calibration mechanism, can not only accurately capture the disturbances of the external environment on the PTC electric heater, but also flexibly adjust the compensation model and control strategy to achieve precise temperature control and optimized energy utilization under extreme conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 The figure is a schematic structural diagram of the electronic function test system of the high-voltage PTC electric heater of the present invention. DETAILED DESCRIPTION

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0037] See also Figure 1 The present invention provides an electronic function test system for a high-voltage PTC electric heater, comprising:

[0038] Step 1: When it is detected that the high-voltage PTC electric heater requires a global environmental disturbance test, the multi-dimensional environmental simulation chamber scheduling interface is called. The programmable temperature control and humidity adjustment unit and variable voltage source are used to controllably switch the temperature, humidity, and grid voltage in the simulation chamber to form multiple extreme and gradual operating conditions. Fixed-point and timed recording is also performed, allowing the multi-channel sensor to output high-resolution operating data in real time.

[0039] The step 1 includes the following:

[0040] Step 101: Programmable structure design of multi-dimensional environment simulation cabin

[0041] The simulation chamber is equipped with programmable heat and cooling sources, an integrated adjustable humidity generator, and a variable voltage source. Composite materials resistant to temperature shocks are selected, and internal insulation and isolation layers are laid out to ensure the chamber's airtightness and safety under harsh operating conditions. An additional air duct system is also included to create temperature or humidity gradients between different areas, enhancing the diversity of simulation scenarios.

[0042] When in use, the programmable heat source, cold source, humidity adjustment unit and voltage control module are combined into one to form a cabin that can apply multiple environmental disturbances at the same time, providing a complex stress scenario for subsequent data collection and environmental compensation, far exceeding the conventional testing capabilities of a single constant temperature or constant humidity chamber.

[0043] Step 102: Precision sensor network deployment and high-resolution data acquisition

[0044] In the simulation cabin, multiple high-sensitivity temperature sensors are distributed in key areas such as high temperature area, low temperature area, and strong convection area, and a temperature sensor array is constructed to collect the cabin temperature T env (t) distribution; humidity sensor arrays are arranged corresponding to the temperature sensors to capture the relative humidity H in the cabin in real time env (t) local gradient and overall change trend;

[0045] A voltage sensor and a current sensor are configured at the power supply inlet and the heater load end, respectively recording the power supply end voltage V env (t) and the current I flowing through the heater load (t) and other environmental parameter components;

[0046] The signals collected by all sensors are based on a unified time base, and each data record is timestamped with τ, (τ∈[0,+∞)), marked with millisecond-level or higher precision. To ensure the reasonable evaluation of the disturbance of each channel, each component can be dimensionally converted or normalized. Alternatively, linear transformation or standardization operations can be introduced before aggregating multi-channel signals, and different dimensions such as environmental parameters can be normalized to similar intervals. Wavelet filtering or feature extraction weighted matrix scale correction can then be performed.

[0047] In order to preliminarily quantify the multi-dimensional environmental intensity, the following environmental comprehensive value E(t) is proposed to quickly characterize the severity of the cabin environment, where the environmental state vector is defined as: Where: T env (t) is the cabin temperature, H env (t) is the relative humidity in the cabin, V env (t) is the voltage at the heater power supply terminal;

[0048] Define the environment gradient vector: Indicates the rate of change of temperature, humidity, and voltage over time, and is used to characterize the severity of the environment in a short period of time;

[0049] Ideally, it can be considered as a derivative in continuous time, but in practical systems, discrete sampling (period Δt) is often used, and the derivative needs to be replaced by the difference:

[0050]

[0051] If the period Δt is not constant or there is data missing, appropriate interpolation or alignment should be performed to avoid error amplification;

[0052] W1 is a 3×3 diagonal or sparse matrix that weights the current state of the environment and is used to reflect the importance of each environmental quantity in different test scenarios. For example:

[0053] W1=diag(α1,α2,α3),α1,α2,α3∈[0,+∞)

[0054] W2 is a 3×3 diagonal or sparse matrix that weights the environmental change rate (gradient) and is used to balance the contributions of the three environmental variables in terms of dynamic severity. For example:

[0055] W2=diag(β1,β2,β3),β1,β2,β3∈[0,+∞)

[0056] p,q are used to define the exponent of the vector norm (usually p,q ≥ 1) and control the penalty method for components of different dimensions;

[0057] κ and γ are two nonlinear amplification factors (κ>0, γ>0), which amplify the cumulative impact of environmental intensity in the final comprehensive index in an exponential form;

[0058] On this basis, the comprehensive environmental value E(t) can be defined as:

[0059]

[0060] When the environmental comprehensive value E(t) is at a low value, it means that in the cumulative observation of [0, t], the environmental variables and their change rates are generally in a relatively stable or slightly disturbed stage;

[0061] When the environmental comprehensive value E(t) rises rapidly and reaches a value much greater than 1, it means that the environmental parameters have been in extreme conditions and / or changed dramatically in the past period of time or at the current moment, and the system needs to increase its vigilance and compensation for temperature control.

[0062] When in use, high-resolution and high-synchronization data acquisition of multi-dimensional environmental disturbances in the simulation cabin is achieved, providing accurate information input for multi-channel data fusion, and E (t ) can be used to quickly determine extreme environmental conditions in subsequent algorithms, thereby improving the sensitivity of the temperature control algorithm to sudden environmental fluctuations.

[0063] Step 2: After the multi-channel original data source is synchronously collected, the data acquisition unit performs noise reduction and normalization operations on the multi-channel signal input based on wavelet filtering and feature vector extraction, and obtains the environmental disturbance compensation ΔT according to the nonlinear mapping operator in the environmental compensation model M. pred (t), so as to output feedforward correction under extreme external disturbances;

[0064] The second step includes the following:

[0065] Step 201: Multi-channel data cleaning and feature extraction

[0066] First, all the original signals are time-aligned to ensure that the cabin temperature T env (t), relative humidity in the cabin H env (t), power supply voltage V env (t) and heater current I load (t) are comparable at the same timestamp τ;

[0067] In order to filter high-frequency noise or harmonic interference while retaining the mutation characteristics that are crucial for subsequent analysis, an adaptive filtering strategy based on wavelet transform is used to decompose each channel signal according to different scales, where: is a wavelet operator, then the temperature signal can be expressed as:

[0068]

[0069] Where: φ(·) is the selected wavelet basis function (Daubechies or Meyer basis functions can be selected according to different requirements);

[0070] s represents the scale factor (s∈(0,+∞)), which captures different frequency components through multi-scale analysis;

[0071] The result can be further reconstructed after filtering in different frequency bands to eliminate random high-frequency noise or low-frequency drift. Similar operations are also applicable to the relative humidity H in the cabin. env (t), power supply voltage V env (t) and heater current I load (t);

[0072] After the above wavelet decomposition and reconstruction, key features are extracted from the multi-channel signal and uniformly recorded as a multi-channel feature vector F(t), which includes several components such as temperature sudden change coefficient, humidity rapid fluctuation coefficient, voltage disturbance intensity and current load distribution;

[0073] Temperature sudden change coefficient: reflects the cabin temperature T env (t) approximates the extreme value of the first-order derivative in a specific period of time, characterizing the transient rate of temperature in the cabin; humidity rapid fluctuation coefficient: measures the relative humidity H in the cabin env (t) Energy peak value in the specific frequency band of wavelet, used to identify high humidity disturbance; Voltage disturbance intensity: voltage V env (t) Conduct short-term energy assessment to measure the transient impact of grid fluctuations on loads;

[0074] Current load distribution: From the heater current I load The average load and fluctuation amplitude are extracted from (t) to determine the power change of the PTC heater.

[0075] Finally: The output of this step includes: the cleaned and reconstructed multi-channel signal is recorded as

[0076] When in use, through wavelet decomposition and reconstruction, it can retain key mutation information of temperature, humidity, voltage and current at multiple scales, and effectively eliminate random noise, ensuring that subsequent model construction is based on data with a higher signal-to-noise ratio. The generated feature vector covers the instantaneous level and dynamic change rate of the environmental state, so that the subsequent compensation model can accurately capture the potential impact of the external environment on the PTC heater.

[0077] Step 202: Construction and output of environmental compensation model

[0078] Integrating multi-channel signals And the components of the multi-channel feature vector F(t), as well as the environmental comprehensive value E(t), form a high-dimensional input vector X(t), which is integrated in the following form:

[0079]

[0080] Here, the multi-channel feature vector F(t) may contain several subcomponents (such as temperature sudden change coefficient, humidity rapid fluctuation coefficient, etc.), which constitute the remaining dimensions of the vector;

[0081] Based on the high-dimensional input vector X(t), an environmental compensation model M is constructed to output the estimated value and compensation value of the temperature deviation, which is recorded as the environmental disturbance compensation value ΔT pred (t), at this time, the environmental compensation model M can be set as a type of high-order kernel regression or deep network function, or a custom high-order polynomial form can be used, as shown in the following example:

[0082]

[0083] Where: φ(·) is a set of nonlinear mapping operators (which can be regarded as high-dimensional kernel functions or hidden layer activations of deep networks) that maps the high-dimensional input vector X(t) to a higher-dimensional feature space; w is the weight vector to be trained or calibrated; ΔT pred (t) is the real-time prediction of the temperature control deviation caused by environmental interference;

[0084] An online learning method is used to iteratively update the weight vector w of the environmental compensation model M. When there is a new measured temperature deviation (by comparing the difference with the actual measured temperature) or more extreme environmental data, the weight vector w is incrementally adjusted to ensure that the compensation model remains sensitive to extreme environments and new operating conditions during long-term operation.

[0085] When used, the wavelet-filtered multi-channel data, the environmental comprehensive value E(t), and the high-order mapping are combined to quickly capture the impact pattern of external disturbances on the PTC heater and output the environmental disturbance compensation in a timely manner, significantly improving the adaptability to complex environments. With the help of dynamic correction and adaptive learning, the model weight vector w of the model continuously approaches the optimal value as the actual working conditions change, overcoming the lag problem of static parameter PID or ordinary linear models in sudden environmental changes.

[0086] The environmental disturbance compensation ΔT calculated by this model under different environmental scenarios (such as extremely low temperature, extremely high humidity, and strong power grid fluctuations) pred (t) will be linked with the real-time temperature control link to correct the heating power setting or calibrate the target parameters of the controller, thereby ensuring that high temperature control accuracy can be maintained under extreme external disturbances.

[0087] Step 3: When the environmental compensation model outputs the environmental disturbance compensation value ΔT pred (t) and the actual temperature T meas When there is a large deviation in (t), the adaptive temperature control algorithm superimposes a feedforward compensation component in the feedforward compensation path and dynamically updates the gain parameter in the feedback control link, so that the control signal u(t) corrects the power output in advance and converges the temperature error quickly;

[0088] The step three includes the following:

[0089] Step 301: Integration of the structural design of the adaptive temperature control algorithm and environmental disturbance compensation

[0090] The environmental disturbance compensation ΔT pred (t) It is considered as an estimate of the potential temperature deviation caused by external factors (such as low temperature, high humidity or unstable power grid) and is integrated into the conventional temperature feedback control, so that the adaptive temperature controller can make feedforward corrections before the temperature deviation fully occurs;

[0091] The control signal u(t) of the adaptive temperature controller is divided into two parts, namely the feedforward compensation component u ff (t) and feedback component u fb (t):

[0092] u(t)=u ff (t)+u fb (t)

[0093] Where: u ff (t) is used to apply power adjustment u in advance when significant environmental disturbance is detected fb (t) then perform conventional closed-loop correction on the actual temperature sampling error;

[0094] In order to highlight creativity and take into account flexibility, the environmental disturbance compensation amount ΔT can be pred (t) passes through the nonlinear mapping operator Ψ(·) and is then multiplied by the gain coefficient Ω(t) that can be dynamically adjusted over time to obtain the feedforward signal:

[0095] u ff (t)=Ω(t)Ψ(ΔT pred (t))

[0096] Where: Ψ(·) is a designable nonlinear function, and a dual-parameter smooth saturation form can be selected to take into account both the linear response to small deviations and the saturation limit to large deviations, which is used to compensate for the environmental disturbance ΔT pred When (t) is large, it is exponentially amplified, and the environmental disturbance compensation amount ΔT pred (t) When it is small, flexible limitation is performed to avoid excessive response;

[0097] Ω(t) is the time-varying gain, which can be adjusted in the range of [0, +∞) according to the actual working conditions (such as the current heater power limit or grid voltage limit) to prevent overshoot or safety hazards in extreme cases;

[0098] The conventional PID or incremental PID controller is transformed into an adaptive PID controller so that its gain coefficient Ω(t) can be gradually corrected during operation.

[0099] Where: The temperature setting value is T set (t), the actual temperature is T meas (t), define the instantaneous temperature error:

[0100] e(t)=T set (t)-T meas (t)

[0101] Example of feedback term using incremental adaptive PID:

[0102]

[0103] Where: u fb (t k ) is the feedback control signal at discrete time t; α p (t k ), α i (t k ), α d (t k ) are proportional, integral and differential gains updated online over time; Δt=t k+1 -t k is the discrete sampling period;

[0104] In order to make the controller aware of the impact of environmental disturbances, the environmental disturbance compensation ΔT generated in the previous step can also be pred (t) Incorporate the gain update rule, for example, if the environmental disturbance compensation ΔT pred (t) If it is too large, you can temporarily increase the differential or proportional gain to speed up the response;

[0105] When used, by simultaneously introducing the feedforward compensation component u ff (t) and the adaptive PID feedback component u fb (t), achieving a double-insurance response to external environmental disturbances: timely intervention before or just when the environment suddenly changes, and continuous fine-tuning in the subsequent actual temperature feedback; the adaptive gain mechanism can dynamically adjust the control strength under different working conditions, so that the system can avoid serious overshoot or lag even in extreme environments.

[0106] Step 302: Online implementation and closed-loop iteration of dynamic control strategy

[0107] In the embedded controller or host computer system, the following operations are performed according to a fixed period Δ(t):

[0108] Get the environmental disturbance compensation ΔT at the current time t pred (tk), actual temperature T meas (t k ), temperature setting value T set (t k ) and the grid voltage V env (t k ) and other information; calculate the feedforward compensation component u according to the feedforward algorithm and feedback algorithm ff (t k ) and feedback component u fb (t k ), and then the total output u(t k ); the total output u(t k ) is converted into actual heater power or duty cycle adjustment signal to control the output heat of the high-voltage PTC electric heater;

[0109] The new power signal causes the heater temperature to change, and the temperature sensor detects the new actual temperature T again. meas (t k+1 ), and uploaded to step 102 (the sensor network and high-resolution data acquisition unit of the first and second steps) and the adaptive temperature control controller of this step;

[0110] At the same time, the environmental disturbance compensation ΔT pred (t) will also be updated in the next sampling period based on the latest environmental data or model prediction (from the online algorithm in the second step); the adaptive PID controller will then combine the current error e(t k+1 ) and environmental disturbance compensation ΔT pred (t k+1 ) Complete the adaptive adjustment of gain;

[0111] Through multiple iterations, a closed loop is formed. When a new large disturbance occurs in the external environment, both the feedforward compensation and the adaptive gain will quickly adapt to maintain the stability and accuracy of temperature control. To highlight the synergy of the algorithm here, the feedforward and feedback signals can be described in the same expression (taking discrete moments as an example):

[0112] u(t k+1 )=Ω(t k )Ψ(ΔT pred (t k ))+u fb (t k+1 )

[0113] The feedback component u fb (t k+1 ) is given by the aforementioned incremental adaptive PID, which shows the feedforward compensation (based on the environmental disturbance compensation ΔT pred (t k )) and adaptive feedback (based on actual temperature error) dual control;

[0114] Finally, the output is an adaptive dynamic control strategy that can be run online and a real-time control signal u(t). This signal actually drives the high-voltage PTC electric heater to achieve the set temperature T set (t) tracking;

[0115] When used, a closed-loop adaptive control loop is formed for actual industrial usage scenarios, especially for conditions with high complexity of multi-dimensional environmental disturbances (such as multiple low-temperature zones, sudden voltage drops or humidity changes, etc.). It has excellent response speed and control accuracy. Through two-way linkage with the second step (compensation model), a dynamic strategy of rapid prediction and real-time correction is formed, and two levels of information (predicted quantity and current measurement error) are used to collaboratively ensure optimal temperature stability.

[0116] Step 4: Once the adaptive control strategy has stabilized the output control signal u(t), extreme scenarios are configured in a multi-dimensional environmental simulation chamber and feedback data is transmitted. After completing the multi-scenario acquisition, the model-controller coupling performance is evaluated based on the cumulative temperature deviation functional γ. Long-term validation is performed to continuously optimize the gain parameters.

[0117] The step 4 includes the following contents:

[0118] Step 401: Multi-environment scenario configuration and data transmission

[0119] Using the multi-dimensional environmental simulation chamber built in the first step, combined with the programmable temperature source, humidity source, and variable voltage source, we systematically configured several extreme test scenarios. Example scenarios include:

[0120] Ultra-low temperature and high humidity startup scenario: for example, below -20℃, relative humidity>90%; strong grid fluctuation scenario: at the power supply end voltage V env (t) Randomly superimpose high-frequency disturbances or simulate sudden voltage drops; Composite scenario: temperature, humidity, and voltage change rapidly together, used to test the robustness of the system under multiple coupled disturbances;

[0121] The actual temperature T is obtained through the sensor network and data acquisition unit in step 1. meas (t), temperature setting value T set (t), environmental parameters, such as cabin temperature T env (t), relative humidity in the cabin H env (t), power supply voltage V env (t), load current I load (t), and the control signal u(t) are recorded synchronously; this process ensures that all important variables related to temperature control have clear timestamps at different times. At the same time, the real-time or periodically summarized test data are sent back to the data processing center for calibration analysis in the next step. The core data finally output includes the temperature response curves T under several sets of scenarios. meas (t), temperature control signal u(t) and various environmental parameter variables (T env (t),H env (t),V env (t),I load (t));

[0122] When in use, it is possible to capture full-factor data of the system in real or simulated extreme environments, providing sufficient samples and measurement basis for subsequent calibration and verification. Multi-scenario testing can more comprehensively reveal potential defects or deficiencies of the system under boundary conditions.

[0123] Step 402: Global error correction and model-controller collaborative tuning

[0124] To characterize the system in the test time interval [0, T test ] The deviation between the temperature set value and the actual measured value reflects the combined effect of environmental parameters (temperature, humidity, voltage, etc.) and their dynamic changes on the control quality. The following quantities are defined:

[0125] Environmental state vector Among them, t∈[0,T test ];

[0126] T env (t) is the external ambient temperature, H env (t) is the external environmental humidity, V env (t) supply voltage to the heater;

[0127] Temperature deviation function: δ(t) = T set (t)-T meas (t), where T set (t) is the set temperature, T meas (t) is the current measured temperature; time gradient information, in order to capture the impact of the environment's changing rate in a short period of time (such as grid transients, voltage disturbances, etc.) on the control effect, introduces the norm of the environment gradient vector:

[0128] Weighting matrix and amplification factors A and B: are matrices that weight the absolute value of the environment and the rate of change of the environment (can be diagonal or sparse symmetric matrices), and their shapes are the same as the dimensions of the environment vector. For example:

[0129] A=diag(a1,a2,a3),B=diag(b1,b2,b3),

[0130] Among them, a i ,b i ≥0, can be configured according to actual test requirements and importance.

[0131] κ1, κ2, and κ3 are all positive nonlinear amplification coefficients (κ i >0), used to amplify or reduce the impact of temperature deviation and environmental disturbance on the overall system;

[0132] p,q≥1, is the norm index, and 1-norm, 2-norm or other higher-order norms can be selected to flexibly adapt to different engineering needs

[0133] Based on the above definition, the cumulative temperature deviation functional γ is constructed as follows:

[0134]

[0135] According to the environmental compensation model M established in the second step and its compensation amount ΔT for environmental disturbance pred If the prediction effect of (t) shows significant deviation in the multi-scenario test, that is, the cumulative temperature deviation functional γ is higher than expected, the feedback data can be used for quadratic regression or online learning update to correct the weight vector w or nonlinear mapping function φ(·) of the environmental compensation model M;

[0136] For the adaptive PID controller or incremental controller defined in step 3, reset the initial gain or adjust the gain update rules based on overshoot, settling time, and other indicators in the test results. For example, the cumulative temperature deviation functional γ can be compared across different scenarios. If the cumulative temperature deviation functional Υ for a particular scenario is significantly higher than the average, indicating that the controller's adaptability to that scenario is insufficient, the gain can be fine-tuned accordingly.

[0137] If the correction of the environmental compensation model results in an environmental disturbance compensation amount ΔT pred If (t) changes significantly, the gain coefficient Ω(t) of the feedforward compensation or the parameters of the nonlinear function Ψ(·) need to be updated synchronously so that the feedforward and feedback can continue to work together and not cancel each other out or over-superpose each other. The final output includes: the updated environmental compensation model M ′ Its core parameters (such as w ′ 、φ ′ (·), etc.), the adjusted adaptive temperature control strategy (including the updated α p (t), α i (t), α d (t), Ω(t), Ψ(·) and other parameters).

[0138] When in use, through the centralized analysis of indicators such as the cumulative temperature deviation functional Υ, an intuitive evaluation of the overall control performance of the system can be achieved. The links with the largest errors or insufficient responses in multiple scenarios can be quickly located, and the environmental compensation model and controller gains can be dynamically adjusted at the same time to ensure that high temperature control accuracy can be maintained under new extreme working conditions, avoiding mismatches caused by modifying only the model or controller separately.

[0139] Step 403: Long-term operation verification and convergence evaluation of self-learning mechanism

[0140] The environmental compensation model-PID controller combination adjusted in the previous step needs to be verified under longer periods and more complex environmental sequences. The simulation chamber can be switched to different temperature ranges, humidity levels, and voltage fluctuation modes over a day or longer to test the stability of the system over a sustained period.

[0141] For example, the cabin temperature is switched between -10°C and 50°C at a random rhythm, accompanied by random voltage disturbances, and the system is recorded to see whether it can maintain indicators such as the cumulative temperature deviation functional γ at a low level during this process.

[0142] Define the convergence index Θ(τ) used to evaluate the gradual convergence of the self-learning algorithm, such as:

[0143] Θ(τ)=||w(τ)-w(τ-Δτ)|| p +|α p (τ)-α p (τ-Δτ)|+…

[0144] Where: w(τ) is the weight vector of the environmental compensation model at time τ, α p (τ) is the proportional gain of the controller, ||·|| p represents the p-norm;

[0145] When the convergence index Θ(τ) approaches a minimum value or a stable interval, it means that the compensation model and the controller parameter update are close to convergence, and the system is also in a steady state in a dynamic environment;

[0146] If the convergence indicator Θ(τ) continues to oscillate or rise during long-term testing, it means that the system cannot learn the appropriate compensation or gain under certain operating conditions. Further modification of the algorithm or constraint conditions, that is, the self-learning convergence indicator, is required, such as reducing the learning step size or limiting the parameter range.

[0147] The temperature response curve T collected during the long-term test meas (t), the cumulative temperature deviation functional Υ, and the convergence index Θ(τ) are visualized and comprehensively analyzed in the data processing center to determine whether the system has achieved the expected industrial application indicators (such as temperature stability, power consumption, and safety protection).

[0148] If long-term verification shows that the system indicators meet or exceed expectations, the overall solution enters the practical stage; if there are still deficiencies, the model and controller strategies in the second and third steps can be further iterated and optimized based on the verification results.

[0149] During use, long-term, multi-round, and cross-scenario testing ensures that the system can maintain good temperature control performance under different day and night temperature gradients and grid load changes, and verifies the continued effectiveness of the adaptive strategy; convergence evaluation provides a key reference for determining whether this solution can operate reliably in the long term in actual projects, and also accumulates real data and experience for possible large-scale applications in the future.

[0150] Through continuous testing and iterative calibration in simulated extreme environments, this solution demonstrates significantly superior temperature control accuracy and safety compared to traditional technologies in real-world industrial applications, enabling adaptive compensation for unconventional environmental disturbances and long-term reliable operation. The close coordination and thorough information sharing between all key elements (environmental simulation chamber, multi-channel data processing, adaptive control algorithm, and dynamic calibration mechanism) ensures the high reliability of the entire solution in handling harsh environments and multi-dimensional dynamic disturbances.

[0151] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0152] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0153] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is only for some logical functions. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0154] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0155] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. Electronic function test system for high voltage PTC electric heater, characterized by: include, When it is detected that the high-voltage PTC electric heater needs to simulate an extreme environment, the multi-dimensional environmental simulation chamber performs programmable temperature and humidity control and variable voltage source operation, periodically adjusts environmental parameters and collects data in real time to generate multi-source basic data containing extreme disturbances; After the data is aggregated, wavelet filtering and feature extraction algorithms are used on the environmental parameter data to generate denoised and normalized signals and to construct an environmental compensation model to output the environmental disturbance compensation amount; When the environmental compensation model outputs the environmental disturbance compensation in real time and detects that the temperature deviation is greater than expected, the adaptive temperature control algorithm combines the feedforward and feedback paths to superimpose the corresponding disturbance information in the online adjustment of the PID gain to form a corrected power control signal; When new environmental disturbance data appears, multi-scenario tests are performed in the environmental simulation chamber and the temperature response curve and environmental disturbance compensation amount are recorded. The model weights and control gain parameters are optimized synchronously based on the cumulative temperature deviation evaluation results.

2. The electronic function test system for a high-voltage PTC electric heater according to claim 1, characterized in that: Programmable heat and cold sources, adjustable humidity generators, and variable voltage sources are installed inside the simulation cabin. Composite materials that are resistant to temperature differences are selected, and thermal insulation and isolation layers are laid out inside, along with an additional air duct system. A sensor array is constructed in the simulation cabin according to high temperature areas, low temperature areas, and strong convection areas to collect environmental parameter components and then construct an environmental comprehensive value. When the environmental comprehensive value rises rapidly, an alert is issued to the outside.

3. The electronic function test system for a high-voltage PTC electric heater according to claim 2, characterized in that: Firstly, all original signals are time-aligned, and an adaptive filtering strategy based on wavelet transform is adopted to decompose each channel signal into different scales. After filtering in different frequency bands, the signals are reconstructed, and the key features are extracted and recorded as multi-channel feature vectors to obtain multi-channel signals.

4. The electronic function test system for a high-voltage PTC electric heater according to claim 3, characterized in that: A high-dimensional input vector is formed by integrating the multi-channel signal, the components of the multi-channel feature vector and the comprehensive value of the environment; An environmental compensation model is constructed based on a high-dimensional input vector to output the environmental disturbance compensation for the temperature deviation. The weight vector of the environmental compensation model is iteratively updated using an online learning method.

5. The electronic function test system for a high-voltage PTC electric heater according to claim 4, characterized in that: The control signal of the adaptive temperature controller is divided into a feedforward compensation component and a feedback component. The environmental disturbance compensation amount passes through a nonlinear mapping operator and is multiplied by a gain coefficient to obtain a feedforward signal. The conventional PID or incremental PID controller is transformed into an adaptive PID controller so that its gain coefficient can be gradually corrected during operation, and the environmental disturbance compensation amount is included in the gain update rule.

6. The electronic function test system for a high-voltage PTC electric heater according to claim 5, characterized in that: The feedforward compensation component and the feedback component are calculated based on the feedforward algorithm and the feedback algorithm to obtain the total output, which is then converted into the actual heater power or duty cycle adjustment signal to control the output heat of the high-voltage PTC electric heater; The environmental disturbance compensation is updated according to the latest environmental data or model prediction, and the adaptive PID controller then combines the current error and the environmental disturbance compensation to complete the adaptive adjustment of the gain.

7. The electronic function test system for a high-voltage PTC electric heater according to claim 6, characterized in that: After configuring several sets of extreme test scenarios, the environmental parameters and control signals are recorded synchronously, and the temperature response curves, temperature control signals and various environmental parameter variables under several sets of scenarios are output; Based on the environmental compensation model and its prediction effect on the environmental disturbance compensation amount, a cumulative temperature deviation functional is constructed; If the accumulated temperature deviation functional is higher than expected, quadratic regression or online learning update is performed using the feedback data to correct the weight vector or nonlinear mapping function of the environmental compensation model.

8. The electronic function test system for a high-voltage PTC electric heater according to claim 7, characterized in that: For the adaptive PID controller, reset the initial gain or adjust the gain update rule based on the overshoot and adjustment time in the test results; If the modification of the environmental compensation model causes a significant change in the amount of environmental disturbance compensation, the gain coefficient of the feedforward compensation or the parameters of the nonlinear function need to be updated synchronously to obtain the adjusted adaptive temperature control strategy.

9. The electronic function test system for a high-voltage PTC electric heater according to claim 8, characterized in that: For the adjusted environmental compensation model-PID controller combination, define a convergence indicator to evaluate the gradual convergence of the self-learning algorithm. If the convergence indicator continues to oscillate or rise during long-term testing, modify the self-learning convergence indicator.

10. The electronic function test system for a high-voltage PTC electric heater according to claim 9, characterized in that: The temperature response curves, cumulative temperature deviation functionals, and convergence indicators collected during long-term testing are visualized and comprehensively analyzed to determine whether the expected industrial application indicators have been achieved. If long-term verification shows that the industrial application indicators do not meet expectations, further iterative optimization can be carried out based on the verification results.

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