Power adjusting system of silicon nitride ceramic electric heating element in DPF regeneration combustor

Abstract

The invention discloses a power adjusting system of a silicon nitride ceramic electric heating element in a DPF regeneration combustor, and relates to the technical field of power adjusting, and the power adjusting system comprises the steps that according to the predicted particulate matter accumulation state, the outdoor temperature and the DPF working condition, the initial target temperature is adjusted to determine the adaptive combustion temperature; performing temperature rise simulation by taking adaptive combustion temperature as a target to determine a standard temperature rise curve, and performing optimization by taking approximation to the standard temperature rise curve as a target to determine an optimal duty ratio sequence; mapping compensation is carried out on the optimal duty ratio sequence according to the thermal hysteresis characteristic of the silicon nitride ceramic electric heating element to determine an adaptive duty ratio sequence, power control is carried out on the silicon nitride ceramic electric heating element, and power fine adjustment is carried out according to the real-time monitoring temperature of a combustor cavity temperature sensor in the control process. The technical problem that in the prior art, the power of the electric heating element cannot be accurately matched with the actual combustion requirement is solved, and the technical effect of improving the accuracy of the temperature rise process and the response efficiency of regeneration control is achieved.

Description

Technical Field

[0001] This invention relates to the field of power regulation technology, and more specifically to a power regulation system for silicon nitride ceramic heating elements in a DPF regenerating burner. Background Technology

[0002] In the regeneration process of a DPF (Diesel Particulate Filter), heating elements are commonly used to heat the combustion chamber to promote the oxidation and combustion of particulate matter, thereby restoring the DPF's filtration performance. Because the particulate load varies significantly depending on vehicle usage, operating environment, and external climate conditions, fixed-temperature or fixed-power heating control methods are difficult to adapt to the changing regeneration requirements. This can easily lead to problems such as excessively high temperatures causing material aging, or insufficient temperatures resulting in incomplete regeneration. Furthermore, silicon nitride ceramic heating elements exhibit significant thermal hysteresis. If this is not combined with precise control of the dynamic temperature rise process, it will result in delayed heating response and decreased temperature control accuracy, thus affecting the DPF regeneration efficiency. Summary of the Invention

[0003] This application provides a power regulation system for silicon nitride ceramic heating elements in a DPF regenerating burner, which addresses the technical problem that existing technologies cannot achieve precise matching between the power of the heating element and actual combustion requirements.

[0004] In view of the above problems, this application provides a power regulation system for silicon nitride ceramic heating elements in a DPF regenerating burner.

[0005] This application provides a power regulation system for silicon nitride ceramic heating elements in a DPF regenerator, the system comprising: The adaptive combustion temperature determination module is used to adjust the initial target temperature based on the predicted particulate matter accumulation state, outdoor temperature, and DPF operating conditions to determine the adaptive combustion temperature. The temperature rise simulation module is used to perform temperature rise simulation with the adaptive combustion temperature as the target, determine a standard temperature rise curve, and optimize the process to approximate the standard temperature rise curve to determine the optimal duty cycle sequence. The mapping compensation module is used to perform mapping compensation on the optimal duty cycle sequence based on the thermal hysteresis characteristics of the silicon nitride ceramic heating element to determine the adaptive duty cycle sequence. The power control module is used to control the power of the silicon nitride ceramic heating element according to the adapted duty cycle sequence, and to fine-tune the power during the control process based on the real-time monitoring temperature of the burner cavity temperature sensor.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: This application adjusts the initial target temperature based on predicted particulate matter accumulation, outdoor temperature, and DPF operating conditions to determine an optimal combustion temperature. Temperature rise simulation is performed with this optimal combustion temperature as the target to determine a standard temperature rise curve. Optimization is then performed to approximate this standard temperature rise curve, determining an optimal duty cycle sequence. Based on the thermal hysteresis characteristics of the silicon nitride ceramic heating element, the optimal duty cycle sequence is mapped and compensated to determine an optimal duty cycle sequence. Power control of the silicon nitride ceramic heating element is performed according to the optimal duty cycle sequence, with power fine-tuning based on real-time monitoring temperature from the burner cavity temperature sensor during the control process. This invention solves the technical problem of existing technologies failing to achieve precise matching between heating element power and actual combustion requirements. By dynamically adjusting the target temperature, optimizing the temperature rise process, and combining thermal hysteresis characteristics with temperature feedback for power regulation, it achieves the technical effect of improving the accuracy of the temperature rise process and the response efficiency of regeneration control. Attached Figure Description

[0007] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 A schematic diagram of the power regulation system structure of the silicon nitride ceramic heating element in the DPF regenerator provided in the embodiments of this application; Figure 2 This is a schematic diagram of the structure of the combustion temperature determination module in the power regulation system of the silicon nitride ceramic heating element in the DPF regenerating burner provided in the embodiments of this application.

[0009] Explanation of reference numerals in the attached diagram: 11 for adaptive combustion temperature determination module, 12 for temperature rise simulation module, 13 for mapping compensation module, and 14 for power control module. Detailed Implementation

[0010] This application provides a power regulation system for silicon nitride ceramic heating elements in a DPF regenerator. It addresses the technical problem that existing technologies cannot achieve precise matching between the power of the heating elements and actual combustion requirements. By dynamically adjusting the target temperature, optimizing the temperature rise process, and combining thermal hysteresis characteristics with temperature feedback for power regulation, the system achieves the technical effect of improving the accuracy of the temperature rise process and the response efficiency of regeneration control.

[0011] The technical solutions of the embodiments of this application 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 this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0012] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0013] Example 1, as Figure 1 As shown in the embodiment of this application, a power regulation system for a silicon nitride ceramic heating element in a DPF regenerator is provided. This system includes: The adaptive combustion temperature determination module 11 is used to adjust the initial target temperature based on the predicted particulate matter accumulation state, outdoor temperature and DPF operating conditions to determine the adaptive combustion temperature.

[0014] In this embodiment, the adaptive combustion temperature determination module 11 first obtains the current exhaust back pressure at both ends of the DPF through a differential pressure sensor, and calculates the particulate load deviation ratio in combination with engine operating parameters to form a predicted particulate accumulation state. Subsequently, it collects operating characteristic data such as DPF usage time, usage frequency, vehicle type, and mileage, and conducts DPF condition analysis with the help of an automotive big data platform, using a clustering algorithm to extract the current DPF condition. Finally, it inputs the predicted particulate accumulation state, outdoor temperature, DPF condition, and initial target temperature into a pre-trained combustion temperature corrector, and uses the nonlinear mapping capability of the feedforward neural network to output the corresponding adaptive combustion temperature.

[0015] Furthermore, such as Figure 2 As shown, in the system provided in the application embodiment, the adaptive combustion temperature determination module 11 further includes: The particulate matter accumulation state prediction unit is used to collect the current exhaust back pressure through differential pressure sensors installed at both ends of the DPF, and determine the particulate load deviation ratio based on the current exhaust back pressure and engine operating parameters as the predicted particulate matter accumulation state; the operating condition analysis unit is used to obtain the DPF usage time, usage frequency, vehicle type, and mileage, and perform DPF operating condition analysis using automotive big data to cluster and obtain the current DPF operating condition; the calibration unit is used to pre-train the combustion temperature calibration device, and determine the appropriate combustion temperature based on the predicted particulate matter accumulation state, outdoor temperature, DPF operating condition, and initial target temperature analysis, wherein the combustion temperature calibration device is built based on a feedforward neural network.

[0016] In this embodiment, the particulate matter accumulation state prediction unit first collects the current exhaust back pressure in real time through differential pressure sensors deployed at both ends of the DPF. This parameter directly reflects the degree of particulate matter blockage within the DPF. Further, it reads engine operating parameters (such as engine speed, load, throttle opening, etc.) between the most recent DPF regenerator burner operation time and the current time. Combined with the exhaust back pressure data, it calculates the first particulate matter load and the second particulate matter load, respectively, and determines the current predicted particulate matter load through data fusion. The current predicted particulate matter load is compared with a preset standard particulate matter load to finally obtain the particulate matter load deviation ratio used to assess the degree of blockage.

[0017] Next, the operating condition analysis unit collects the DPF's usage duration and frequency, which are directly read from the operation log recorded by the DPF controller. Vehicle type information is obtained from the vehicle configuration file (e.g., VIN code decoding). Mileage is obtained by reading the cumulative data from the vehicle's odometer. These multi-dimensional operating parameters are input into the automotive big data platform, where K-means clustering is used to group similar DPF usage patterns. This algorithm aims to minimize the Euclidean distance between the centers of each operating condition, ultimately outputting the current DPF's operating condition classification result. This DPF operating condition comprehensively reflects the state level of the DPF during actual operation, including information such as its thermal aging degree, particle deposition trend, and changes in heat transfer performance.

[0018] The correction unit then intelligently corrects the target temperature using a combustion temperature corrector. This corrector is constructed using a feedforward neural network, and historical regeneration process samples are used for modeling during the training phase. Input variables for the training data include the predicted particulate matter accumulation state, outdoor temperature (collected by an external temperature sensor), DPF conditions (clustering labels output by the condition analysis unit), and the initial target temperature (set by the control strategy); the output variable is the adapted combustion temperature obtained through actual verification. The network training method employs a standard backpropagation algorithm, using the mean squared error as the loss function, and adjusting the connection weights of the neural network through gradient descent to make the output approximate the target temperature. In the practical application phase, the correction unit synchronously inputs the above four types of input data into the trained combustion temperature corrector, performs forward inference calculations, and outputs the adapted combustion temperature.

[0019] Furthermore, in the system provided in the application embodiments, the particulate matter accumulation state prediction and determination unit further includes: The first particulate load determination subunit is used to determine the first particulate load based on the current exhaust back pressure analysis; the second particulate load determination subunit is used to read the engine operating parameters within the time interval between the most recent DPF regenerator working time and the current time, and analyze and determine the second particulate load; the calculation subunit is used to determine the predicted particulate load based on the analysis of the first particulate load and the second particulate load, and calculate the particulate load deviation ratio based on the preset standard particulate load.

[0020] In this embodiment, the first particulate load determination subunit obtains the current exhaust back pressure by reading the differential pressure sensors installed at both ends of the DPF, using a lookup table method. Specifically, the exhaust back pressure value is input into a pre-established back pressure-particulate load lookup table, which is calibrated through engine bench testing. The corresponding particulate load value is found based on the position of the current back pressure in the table, thereby determining the first particulate load.

[0021] The second particulate load determination subunit employs a particulate generation estimation method based on a regression model to establish a functional mapping between engine behavior and particulate accumulation. This subunit first obtains the time interval between the most recent DPF regenerator burner operation and the current time. Within this time interval, engine operating parameters, including engine speed, injection pulse width, intake pressure, and load rate, are extracted via the CAN bus. This data is then input into a particulate generation regression model constructed using a multivariate linear regression algorithm. During the model training phase, a large amount of historical operating data is incorporated. The input variables during training are the aforementioned engine operating parameters, and the output variable is the actual particulate emissions measured by a particulate trap or exhaust gas analyzer. After training, the model can quickly predict the total particulate generation within the regeneration interval during actual operation, thereby outputting the second particulate load.

[0022] The subsequent calculation sub-unit uses a linear weighted average method to fuse the first and second particulate loads. This method sets the weight of the first particulate load to α (e.g., 0.6) and the weight of the second particulate load to 1−α (e.g., 0.4). Through weighted calculation, the predicted particulate load is obtained. Next, the difference between the predicted particulate load and the preset standard particulate load is calculated, and the absolute value of the difference is divided by the preset standard particulate load to obtain the particulate load deviation ratio. The preset standard particulate load is pre-set by technical experts.

[0023] The temperature rise simulation module 12 is used to perform temperature rise simulation with the target of the adaptive combustion temperature, determine the standard temperature rise curve, and perform optimization with the aim of approximating the standard temperature rise curve to determine the optimal duty cycle sequence.

[0024] In this embodiment, the temperature rise simulation module 12 is used to construct a temperature rise process control strategy around the adapted combustion temperature, specifically including two stages: temperature rise simulation modeling and heating control parameter optimization. In the first stage, the temperature rise simulation module 12 first performs operational simulation modeling on the DPF regenerator, constructing a burner operation simulation model that can reflect the heating response characteristics. Subsequently, based on the predicted particulate matter accumulation state, outdoor temperature, and DPF operating conditions, the operation simulation model is parameterized to form an adapted operation simulation model that can accurately reflect the current operating conditions. With the adapted combustion temperature as the target input, the adapted operation simulation model is used to perform numerical simulation of the temperature rise process, and finally outputs a standard temperature rise curve to describe the ideal temperature change trajectory over time during the electrothermal heating process.

[0025] In the second stage, the temperature rise simulation module 12, with the optimization objective of approximating the standard temperature rise curve, obtains the power regulation time interval and duty cycle adjustment parameter space of the DPF regenerator burner, randomly generates the first set of duty cycle control sequences, and inputs them into the adaptation operation simulation model to simulate the corresponding first simulated temperature rise curve. Subsequently, using the standard temperature rise curve as a benchmark, the error of the first simulated temperature rise curve is calculated to obtain the first temperature rise deviation. Based on this deviation value, the temperature rise simulation module 12 uses an iterative optimization algorithm to continuously generate and evaluate new duty cycle sequences. Through multiple rounds of iteration, the set with the smallest temperature rise deviation is selected as the output result, and finally the optimal duty cycle sequence that can achieve optimal control of the temperature rise process is determined.

[0026] Furthermore, in the system provided in the application embodiment, the temperature rise simulation module 12 also includes: The simulation modeling unit is used to perform operational simulation modeling of the DPF regenerator and construct an operational simulation model of the burner; the parameter rendering unit is used to perform parameter rendering of the operational simulation model based on the predicted particulate matter accumulation state, outdoor temperature, and DPF operating conditions to obtain an adapted operational simulation model; the standard temperature rise curve determination unit is used to use the adapted operational simulation model to perform temperature rise simulation with the adapted combustion temperature as the target and determine the standard temperature rise curve.

[0027] In this embodiment, the simulation modeling unit is used to perform operational simulation modeling of the DPF regenerator burner, constructing an operational simulation model of the burner for calculating the temperature rise process. This unit employs a thermal stratification modeling method, dividing the DPF regenerator burner into multiple thermal characteristic regions, such as the silicon nitride ceramic heating element region, the DPF main structure region, and the shell convection heat dissipation region, assigning fixed heat capacity and thermal resistance parameters to each region. This method establishes layered heat conduction paths to simulate the heat transfer process within the structure during heating, resulting in a structured thermal simulation model. Finally, the operational simulation model of the burner is obtained through this modeling process.

[0028] Next, the parameter rendering unit renders the above-mentioned operational simulation model based on the predicted particulate matter accumulation state, outdoor temperature, and DPF operating conditions. This process employs a parameter replacement method, mapping the real-time operating state to the model parameters to enable the simulation model to respond under the current operating conditions. Specifically, firstly, the heat capacity parameter of the DPF region is adjusted according to the predicted particulate matter accumulation state; the more particulate matter, the greater the heat required per unit temperature rise. Secondly, the outdoor temperature is written into the model as the initial boundary condition to reflect the influence of the current ambient temperature on the heat conduction initiation point. Finally, based on the current DPF operating conditions output by the operating condition analysis unit, the thermal resistance parameter in the model is updated to simulate the physical behavior of decreased thermal conductivity after DPF aging. Through the replacement and injection of these three types of parameters, an adapted operational simulation model is obtained.

[0029] Finally, the standard temperature rise curve determination unit uses the aforementioned adaptation simulation model to conduct temperature rise simulation. This process employs a constant power input method, where a fixed power (e.g., 100% duty cycle) is applied to the silicon nitride ceramic heating element region within the adaptation simulation model, and time-step calculations are performed. During the simulation, a node representing the center position of the DPF is selected as the observation point, and its temperature change over time is recorded. The temperature-time data is then plotted as a curve, which is the standard temperature rise curve.

[0030] Furthermore, in the system provided in the application embodiment, the temperature rise simulation module 12 also includes: The system includes a data acquisition unit for acquiring the power adjustment time interval and duty cycle adjustment parameter space of the DPF regenerator; a first duty cycle sequence generation unit for randomly generating a first duty cycle sequence based on the power adjustment time interval and duty cycle adjustment parameter space; a simulation operation unit for performing a simulation operation using the adapted operation simulation model based on the first duty cycle sequence to obtain a first simulated temperature rise curve; a deviation calculation unit for calculating the deviation of the first simulated temperature rise curve based on the standard temperature rise curve to determine a first temperature rise deviation; and an iterative optimization unit for iteratively optimizing based on the first temperature rise deviation to determine the optimal duty cycle sequence.

[0031] In this embodiment, the data acquisition unit first acquires the power adjustment time interval and duty cycle adjustment parameter space of the DPF regenerator. The power adjustment time interval refers to the duration of each control cycle, employing a periodic time-division control method commonly used in thermal control systems, for example, set to 30 seconds, meaning the power control command is updated every 30 seconds. The duty cycle adjustment parameter space defines the energizing time range of the silicon nitride ceramic heating element within each cycle, for example, set to 10% to 100%, with a step size of 5%. These parameters are obtained by reading the control strategy setting file or the configuration table in the embedded control module and are used as the search boundary for subsequent duty cycle sequence generation and optimization.

[0032] Next, the first duty cycle sequence generation unit uses a random number generation method to construct an initial control sequence based on the obtained adjustment interval and duty cycle space. This sequence is a set of duty cycle values ​​within several time steps, with each value randomly selected from a preset parameter space to simulate the temperature rise behavior under different power strategies.

[0033] Subsequently, the simulation unit uses the previously generated first duty cycle sequence as control input to import it into the pre-constructed adaptive simulation model. This simulation model, built by the simulation modeling unit and the parameter rendering unit, possesses the current particulate matter state, DPF operating conditions, and ambient temperature response characteristics. The simulation unit injects duty cycle data step by step according to the control cycle, converting the duty cycle value into an equivalent constant power input within each cycle, and calculating the temperature response of the silicon nitride ceramic heating element region. Finally, after the simulation cycle ends, the first simulated temperature rise curve is output.

[0034] Then, the deviation calculation unit uses the pre-generated standard temperature rise curve as the target benchmark to perform deviation analysis on the first simulated temperature rise curve. This process adopts a one-to-one correspondence method between temperature and time points, comparing the difference between the simulated temperature and the target temperature at each time point, and calculating the cumulative deviation of the entire process to obtain the first temperature rise deviation.

[0035] Finally, the iterative optimization unit performs iterative optimization based on the first temperature rise deviation. Specifically, within the set power adjustment time interval and duty cycle adjustment parameter space, new duty cycle sequences are continuously generated, and the temperature rise deviation is repeatedly calculated for each sequence until the preset number of iterations is completed, obtaining multiple duty cycle sequences and their corresponding temperature rise deviation values. Finally, the sequence with the smallest temperature rise deviation is selected from all candidate sequences as the optimal duty cycle sequence under the current operating condition.

[0036] Furthermore, in the system provided in the application embodiments, the iterative optimization unit further includes: The temperature rise deviation calculation subunit is used to iteratively select the duty cycle sequence based on the power adjustment time interval and the duty cycle adjustment parameter space, and repeatedly calculate the temperature rise deviation until the preset number of iterations is reached, so as to obtain multiple duty cycle sequences and multiple temperature rise deviations; the selection subunit is used to select the duty cycle sequence with the smallest temperature rise deviation as the optimal duty cycle sequence.

[0037] In this embodiment, the temperature rise deviation calculation subunit first selects a new duty cycle sequence using an iterative sampling method within the acquired power adjustment time interval and duty cycle adjustment parameter space. This process uses a fixed duty cycle step size (e.g., 5%) and adjustment cycle number (e.g., 8 cycles). Each iteration generates a new duty cycle sequence from the parameter space, ensuring coverage of various heating rhythm combinations. For each sequence generated, the simulation unit executes a temperature rise simulation, and the deviation calculation unit calculates the temperature response deviation, i.e., the temperature rise deviation, corresponding to the sequence, using the standard temperature rise curve as a benchmark.

[0038] The above iterative process continues until the preset number of iterations (e.g., 100 rounds) is reached. Finally, the temperature rise deviation calculation subunit outputs multiple duty cycle sequences and their corresponding temperature rise deviation values, forming a one-to-one optimization candidate set for subsequent screening.

[0039] Next, the sub-unit is selected to perform the optimal solution extraction operation in the candidate set. This unit uses the minimum value screening method to compare all temperature rise deviation values ​​and selects the duty cycle sequence with the smallest corresponding deviation as the final optimal duty cycle sequence.

[0040] The mapping compensation module 13 is used to perform mapping compensation on the optimal duty cycle sequence based on the thermal hysteresis characteristics of the silicon nitride ceramic heating element, and determine the suitable duty cycle sequence.

[0041] In this embodiment, the mapping compensation module 13 performs response correction on the generated optimal duty cycle sequence based on the thermal hysteresis characteristics of the silicon nitride ceramic heating element. This module first determines the initial hysteresis compensation coefficient and then corrects it based on the current outdoor temperature to obtain an adaptive hysteresis compensation coefficient, thereby constructing a response compensation filter. Finally, the optimal duty cycle sequence is mapped and compensated using this filter to output an adapted duty cycle sequence.

[0042] Furthermore, in the system provided in the application embodiment, the mapping compensation module 13 also includes: The initial hysteresis compensation coefficient determination unit is used to determine the initial hysteresis compensation coefficient based on the thermal hysteresis characteristics matching of the silicon nitride ceramic heating element; the adaptation hysteresis compensation coefficient determination unit is used to adjust the initial hysteresis compensation coefficient according to the outdoor temperature, determine the adaptation hysteresis compensation coefficient, and construct a response compensation filter; the adaptation duty cycle sequence determination unit is used to use the response compensation filter to smooth and feedforward weight the optimal duty cycle sequence, and output the adaptation duty cycle sequence.

[0043] In this embodiment, the initial hysteresis compensation coefficient determination unit is used to determine the initial hysteresis compensation coefficient based on the thermal hysteresis characteristics of the silicon nitride ceramic heating element. The unit employs a performance parameter matching method combined with a database query mechanism. Specifically, by identifying the model information of the silicon nitride ceramic heating element (such as chip ID, configuration file, or controller setting parameters), historical thermal response data corresponding to that model is extracted from the built-in heating element response performance database. This database contains parameters such as specific heat capacity, thermal conductivity, structural dimensions, and typical heating hysteresis time for multiple heating element models. Based on the element model matching, the recommended hysteresis compensation coefficient range from the historical data is located, and the median value is selected as the current initial hysteresis compensation coefficient according to the actual application conditions of the element (such as installation location and heating direction). For example, if a matching element of model NSiC-EH150 is found, its hysteresis time is between 15 and 18 seconds, and the corresponding recommended compensation coefficient K is between 0.4 and 0.5, K=0.45 is directly set as the initial compensation coefficient.

[0044] Subsequently, the adaptation hysteresis compensation coefficient determination unit adjusts the initial hysteresis compensation coefficient based on the outdoor temperature to determine the adaptation hysteresis compensation coefficient and constructs a response compensation filter. This unit employs a temperature range correction method, acquiring the current outdoor temperature through an external temperature sensor and using a preset temperature-gain relationship table for segmented correction. When the ambient temperature is above 10℃, K remains unchanged; between 0 and 10℃, the initial K is multiplied by 0.9; below 0℃, K is multiplied by 1.2 to enhance the compensation response to low-temperature heat dissipation environments. For example, if the initial hysteresis compensation coefficient is K = 0.45 and the current temperature is −5℃, then the corrected K = 0.45 × 1.2 = 0.54. After correction, the adaptation hysteresis compensation coefficient is used as a weighting coefficient to construct a response compensation filter, the structure of which is as follows: ,in, This represents the duty cycle for the current period. Let K be the duty cycle for the next cycle, and K be the adjusted adaptation lag compensation coefficient. This is the duty cycle output value after compensation and correction for the current cycle.

[0045] Finally, the adaptive duty cycle sequence determination unit uses a response compensation filter to smooth and feedforward weight the optimal duty cycle sequence, outputting the adapted duty cycle sequence to ensure that the temperature rise control process is both stable and predictable. Specifically, the adaptive duty cycle sequence determination unit first performs sliding window smoothing on the optimal duty cycle sequence, using a three-cycle weighted average to reduce instantaneous jumps and avoid system instability caused by sudden changes in the PWM signal. Then, the smoothed sequence is input into the constructed response compensation filter, which performs feedforward weighting calculation on the duty cycle signal at each moment, pre-adding the power change trend of the next cycle to the current output based on the current K value. For example, if the optimal sequence is [70%, 80%, 85%, 80%], after processing with K=0.54, [75%, 83%, 84%, 80%] can be obtained, achieving timing shift of the control signal without changing the overall strategy structure. Finally, the adapted duty cycle sequence is output.

[0046] The power control module 14 is used to control the power of the silicon nitride ceramic heating element according to the adapted duty cycle sequence, and to fine-tune the power according to the real-time monitoring temperature of the burner cavity temperature sensor during the control process.

[0047] In this embodiment, the power control module 14 controls the power of the silicon nitride ceramic heating element according to the adapted duty cycle sequence. During this process, the adapted duty cycle sequence output by the adapted duty cycle sequence determination unit is invoked, and a corresponding PWM control signal is generated according to the duty cycle value of the current cycle to drive the power electronic device to control the current output, thereby controlling the power of the silicon nitride ceramic heating element.

[0048] Subsequently, the burner cavity temperature is collected in real time by a burner cavity temperature sensor, and this temperature is compared with the optimal simulated temperature rise curve corresponding to the optimal duty cycle sequence to obtain the current real-time temperature difference. Based on the current temperature rise stage, the duty cycle value for the next control cycle is slightly corrected to achieve power fine-tuning.

[0049] Furthermore, in the system provided in the application embodiment, the power control module 14 further includes: The optimal simulated temperature rise curve acquisition unit is used to acquire the optimal simulated temperature rise curve of the optimal duty cycle sequence; the real-time monitoring unit is used to monitor and acquire the real-time temperature using the burner cavity temperature sensor; the real-time temperature difference acquisition unit is used to calculate and acquire the real-time temperature difference based on the current temperature rise stage and the optimal simulated temperature rise curve; and the fine-tuning unit is used to fine-tune the duty cycle of the next control node based on the current temperature rise stage and the real-time temperature difference.

[0050] In this embodiment, the optimal simulated temperature rise curve acquisition unit is first used to acquire the optimal simulated temperature rise curve of the optimal duty cycle sequence. This unit takes the generated optimal duty cycle sequence as input, calls the adaptive operation simulation model, performs a complete temperature rise process simulation under the set adaptive combustion temperature conditions, outputs the temperature prediction value corresponding to each control cycle in the simulation environment, and constructs it into a continuous optimal simulated temperature rise curve with time-series identification.

[0051] The real-time monitoring unit then uses a burner cavity temperature sensor to monitor and acquire the real-time temperature. This unit is deployed in the key thermal response area of ​​the burner cavity and uses a high-speed response temperature sensor (such as a K-type thermocouple or a digital temperature probe) to acquire the actual cavity temperature at the end of the current cycle, using the collected temperature value as the real-time temperature feedback data for the current cycle.

[0052] Next, the real-time temperature difference acquisition unit calculates the real-time temperature difference based on the current temperature rise stage and the optimal simulated temperature rise curve as a reference. Specifically, it first identifies the current temperature rise stage (such as the heating stage, the constant temperature stage, or the slow cooling stage), then extracts the target temperature point for the current period from the optimal simulated temperature rise curve, compares it with the actual temperature collected by the real-time monitoring unit, and outputs the real-time temperature difference for the current period.

[0053] Finally, the fine-tuning unit fine-tunes the duty cycle of the next control node based on the current temperature rise stage and the real-time temperature difference. During this process, corresponding adjustment sensitivities and upper and lower limits are set according to different temperature rise stages, the duty cycle is adjusted appropriately, and iterative corrections are continuously made until the DPF regenerator burner operation process ends, thereby achieving precise power regulation and closed-loop temperature control correction throughout the entire process.

[0054] Furthermore, the system provided in the application embodiments also includes: Based on the preset segmented fine-tuning compensation mechanism, the duty cycle of the next control node is fine-tuned according to the current temperature rise stage and the real-time temperature difference until the DPF regenerator burner completes its operation.

[0055] In this embodiment, the heating process is first divided into an initial heating stage, a constant temperature stage, and a gradual cooling stage. For the differences in temperature control characteristics at each stage, corresponding adjustment strategies are pre-set, forming a pre-defined segmented fine-tuning compensation mechanism. This mechanism configures differentiated adjustment parameters for different temperature rise stages, such as the temperature difference-duty cycle response coefficient and the maximum adjustment range limit. For example, in the initial heating stage, a high response coefficient (e.g., adjusting the duty cycle by 2% for every 1°C temperature difference) is set to accelerate the temperature rise; in the constant temperature stage, to prevent temperature fluctuations, a low response coefficient (e.g., 1% / °C) is set, and the adjustment range is limited to within ±3%.

[0056] Within the control cycle, the real-time temperature difference acquisition unit first outputs the real-time temperature difference ΔT for the current cycle. Then, the fine-tuning unit identifies the current temperature rise stage and invokes the corresponding adjustment rule in the preset segmented fine-tuning compensation mechanism. Based on the sign and magnitude of ΔT, the adjustment range of the duty cycle for the next control node is calculated and added to the original duty cycle value to form the updated control command. For example, if the current duty cycle is 75%, ΔT is -4°C, the current temperature is constant, and the adjustment coefficient is 1% / °C, then the adjusted duty cycle is 79%; if ΔT is +6°C, the limit is lowered to 72% (limited by ±3%).

[0057] This fine-tuning process is executed iteratively in each cycle, continuously optimizing the accuracy and stability of control commands. The entire fine-tuning mechanism takes effect from the start of heating until the DPF regenerator burner finishes operation, ensuring that temperature control always closely follows the optimal simulated temperature rise curve and can dynamically compensate for factors such as changes in the external environment and actual response lag, thereby effectively improving temperature control accuracy and response speed.

[0058] In summary, the embodiments of this application have at least the following technical effects: This application adjusts the initial target temperature based on predicted particulate matter accumulation, outdoor temperature, and DPF operating conditions to determine an optimal combustion temperature. Temperature rise simulation is performed with this optimal combustion temperature as the target to determine a standard temperature rise curve. Optimization is then performed to approximate this standard temperature rise curve, determining an optimal duty cycle sequence. Based on the thermal hysteresis characteristics of the silicon nitride ceramic heating element, the optimal duty cycle sequence is mapped and compensated to determine an optimal duty cycle sequence. Power control of the silicon nitride ceramic heating element is performed according to the optimal duty cycle sequence, with power fine-tuning based on real-time monitoring temperature from the burner cavity temperature sensor during the control process. This invention solves the technical problem of existing technologies failing to achieve precise matching between heating element power and actual combustion requirements. By dynamically adjusting the target temperature, optimizing the temperature rise process, and combining thermal hysteresis characteristics with temperature feedback for power regulation, it achieves the technical effect of improving the accuracy of the temperature rise process and the response efficiency of regeneration control.

[0059] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0060] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0061] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A power regulation system for silicon nitride ceramic heating elements in a DPF regenerating burner, characterized in that, The power regulation system of the silicon nitride ceramic heating element includes: The adaptive combustion temperature determination module is used to adjust the initial target temperature based on the predicted particulate matter accumulation state, outdoor temperature, and DPF operating conditions to determine the adaptive combustion temperature. The temperature rise simulation module is used to simulate the temperature rise with the target of the adapted combustion temperature, determine the standard temperature rise curve, and perform optimization with the aim of approximating the standard temperature rise curve to determine the optimal duty cycle sequence. The mapping compensation module is used to perform mapping compensation on the optimal duty cycle sequence based on the thermal hysteresis characteristics of the silicon nitride ceramic heating element, and determine the suitable duty cycle sequence. The power control module is used to control the power of the silicon nitride ceramic heating element according to the adapted duty cycle sequence, and to fine-tune the power based on the real-time monitoring temperature of the burner cavity temperature sensor during the control process.

2. The power regulation system for the silicon nitride ceramic heating element in the DPF regenerator according to claim 1, characterized in that, The adaptive combustion temperature determination module includes: The particulate matter accumulation state prediction unit is used to collect the current exhaust back pressure through differential pressure sensors installed at both ends of the DPF, and determine the particulate matter load deviation ratio based on the current exhaust back pressure and engine operating parameters as the predicted particulate matter accumulation state. The operating condition analysis unit is used to obtain the usage time and number of times of DPF, as well as the vehicle type and mileage of the vehicle to which it belongs. It uses big data of automobiles to perform DPF operating condition analysis and clusters to obtain the current DPF operating condition. The calibration unit is used to pre-train the combustion temperature corrector and determine the appropriate combustion temperature based on the predicted particulate matter accumulation state, outdoor temperature, DPF conditions and initial target temperature analysis. The combustion temperature corrector is constructed based on a feedforward neural network.

3. The power regulation system for the silicon nitride ceramic heating element in the DPF regenerator according to claim 2, characterized in that, The predictive particulate matter accumulation state determination unit includes: The first particulate load determination subunit is used to determine the first particulate load based on the current exhaust back pressure analysis. The second particulate load determination subunit is used to read the engine operating parameters within the time interval between the most recent DPF regenerator working time and the current time, and analyze and determine the second particulate load. The calculation subunit is used to determine the predicted particulate load based on the analysis of the first particulate load and the second particulate load, and to calculate the particulate load deviation ratio based on the preset standard particulate load.

4. The power regulation system for the silicon nitride ceramic heating element in the DPF regenerator according to claim 1, characterized in that, The temperature rise simulation module includes: The simulation modeling unit is used to perform operational simulation modeling of the DPF regenerator and build an operational simulation model of the burner. The parameter rendering unit is used to perform parameter rendering on the operation simulation model based on the predicted particulate matter accumulation state, outdoor temperature and DPF conditions, and obtain an adapted operation simulation model. The standard temperature rise curve determination unit is used to perform temperature rise simulation with the adapted combustion temperature as the target using the adapted operation simulation model, and determine the standard temperature rise curve.

5. The power regulation system for the silicon nitride ceramic heating element in the DPF regenerator according to claim 4, characterized in that, The temperature rise simulation module includes: The data acquisition unit is used to acquire the power regulation time interval and duty cycle adjustment parameter space of the DPF regenerator burner; The first duty cycle sequence generation unit is used to randomly generate a first duty cycle sequence based on the power adjustment time interval and the duty cycle adjustment parameter space. The simulation operation unit is used to perform a simulation operation based on the first duty cycle sequence using the adapted operation simulation model to obtain a first simulated temperature rise curve. The deviation calculation unit is used to calculate the deviation of the first simulated temperature rise curve based on the standard temperature rise curve, and determine the first temperature rise deviation. The iterative optimization unit is used to perform iterative optimization based on the first temperature rise deviation to determine the optimal duty cycle sequence.

6. The power regulation system for the silicon nitride ceramic heating element in the DPF regenerator according to claim 5, characterized in that, Iterative optimization unit, including: The temperature rise deviation calculation subunit is used to iteratively select the duty cycle sequence based on the power adjustment time interval and the duty cycle adjustment parameter space, and repeatedly calculate the temperature rise deviation until the preset number of iterations is reached, so as to obtain multiple duty cycle sequences and multiple temperature rise deviations. Select a sub-unit to select the duty cycle sequence with the minimum temperature rise deviation as the optimal duty cycle sequence.

7. The power regulation system for the silicon nitride ceramic heating element in the DPF regenerator according to claim 1, characterized in that, The mapping compensation module includes: The initial hysteresis compensation coefficient determination unit is used to determine the initial hysteresis compensation coefficient based on the thermal hysteresis characteristics matching of the silicon nitride ceramic heating element. The adaptation hysteresis compensation coefficient determination unit is used to adjust the initial hysteresis compensation coefficient according to the outdoor temperature, determine the adaptation hysteresis compensation coefficient, and construct a response compensation filter; The adaptive duty cycle sequence determination unit is used to smooth and feedforward weight the optimal duty cycle sequence using the response compensation filter, and output the adaptive duty cycle sequence.

8. The power regulation system for the silicon nitride ceramic heating element in the DPF regenerator according to claim 1, characterized in that, The power control module includes: The optimal simulated temperature rise curve acquisition unit is used to acquire the optimal simulated temperature rise curve of the optimal duty cycle sequence. A real-time monitoring unit is used to monitor and acquire the real-time temperature using the burner cavity temperature sensor; The real-time temperature difference acquisition unit is used to calculate and acquire the real-time temperature difference based on the current temperature rise stage and the optimal simulated temperature rise curve as a reference. The fine-tuning unit is used to fine-tune the duty cycle of the next control node based on the current temperature rise stage and the real-time temperature difference.

9. The power regulation system for the silicon nitride ceramic heating element in the DPF regenerator according to claim 8, characterized in that, Based on the preset segmented fine-tuning compensation mechanism, the duty cycle of the next control node is fine-tuned according to the current temperature rise stage and the real-time temperature difference until the DPF regenerator burner completes its operation.

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