Method, system, equipment and medium for updating calibration quantity of DPF (diesel particulate filter) carbon loading calculation model of diesel engine

Abstract

The invention relates to the technical field of diesel engine carbon loading capacity calculation, in particular to a calibration quantity updating method, system and device for a diesel engine DPF carbon loading capacity calculation model and a medium, and the method comprises the steps that different test conditions are set to execute diesel engine DPF carbon loading tests, multiple sets of test data are obtained, and target accuracy is set; performing single-group data calibration on each group of test data to obtain a single-group data target calibration quantity and a calibration area; calibrating a plurality of groups of data to obtain a final target calibration quantity under each experimental condition; each final target calibration quantity is written into the control logic of the corresponding test condition in the ECU program, and calibration quantity updating is completed. According to the method, the shared calibration quantity is solved based on the multi-target optimization algorithm through automatic single-group data calibration, the final target calibration quantity meeting various test conditions can be obtained and written into the ECU program, efficient and accurate calibration is achieved, and the precision requirement under the multiple groups of test conditions can be systematically met.

Description

Technical Field

[0001] This application relates to the field of diesel engine carbon load calculation technology, specifically to a method, system, equipment, and medium for updating the calibration value of a diesel engine DPF carbon load calculation model. Background Technology

[0002] Diesel engines are widely used in commercial vehicles and industrial sectors due to their high torque and fuel economy. To meet increasingly stringent emission regulations, diesel engine aftertreatment systems are typically equipped with diesel particulate filters (DPFs) to capture and reduce particulate emissions. Accurately calculating the carbon loading in the DPF is crucial for achieving efficient regeneration and avoiding filter clogging; therefore, reliable carbon loading calculation models and precise calibration methods are required.

[0003] In existing technologies, the calibration of carbon loading calculation models is typically accomplished by experimenters manually adjusting the calibration values ​​in the electronic control unit (ECU) program based on their experience. Experimenters gradually modify the calibration values ​​based on experimental data, such as actual carbon weight gain measurements, to make the output of the carbon loading calculation model as close as possible to the actual value. This method relies on the expertise of the experimenters and repeated trials, and under certain conditions, it can achieve a certain level of calibration accuracy.

[0004] However, manually adjusting the calibration quantile is time-consuming and labor-intensive, and the results are heavily influenced by personal experience, leading to low calibration efficiency and inconsistent results. Especially when processing data from multiple sets of different experimental conditions, existing methods struggle to systematically determine a shared calibration quantile that simultaneously meets the accuracy requirements under all experimental conditions. This limits the automation and optimization capabilities of the calibration process, impacting the accuracy and reliability of carbon loading calculation models in practical applications. Summary of the Invention

[0005] To address the technical problem that existing carbon load calculation models rely on manual adjustment of calibration values, which is inefficient and makes it difficult to systematically determine a shared calibration value that can simultaneously meet the accuracy requirements of multiple sets of different test conditions, this application provides a calibration value update method, system, equipment, and medium for diesel engine DPF carbon load calculation models. By automating single-set data calibration and solving the shared calibration value based on a multi-objective optimization algorithm, the final target calibration value that meets various test conditions can be obtained and written into the ECU program, achieving efficient and accurate calibration and systematically meeting the accuracy requirements under multiple test conditions.

[0006] In a first aspect, this application provides a method for updating the calibration value of a diesel engine DPF carbon load calculation model, comprising the following steps: S1: Set different test conditions to perform diesel engine DPF carbon loading test, obtain multiple sets of test data, and set the target accuracy; The experimental data are actual measured values ​​of carbon weight gain; S2: Perform individual data calibration for each set of experimental data, including: Based on the test conditions of this set of test data, the target calibration quantity is determined from the ECU program; The test conditions and test data are input into the preset diesel engine DPF carbon load calculation model. By calling the control logic in the ECU program, the carbon weight gain of the model under the target calibration is calculated. The carbon weight gain error between the calculated carbon weight gain and the actual carbon weight gain; Determine whether the absolute value of the carbon weight gain error is less than or equal to the target accuracy. If it is, output the target calibration amount and calibration region for the single set of experimental data. Otherwise, determine the calibration region based on the kernel density estimation algorithm and iteratively update the target calibration amount until the error meets the requirements, and obtain the target calibration amount and calibration region for the single set of experimental data. S3: Perform multiple sets of data calibration, including: Determine whether the calibration regions of each group of experimental data overlap; When the calibration regions overlap, an optimization problem is constructed based on the overlapping regions, and the NSGA-II multi-objective optimization algorithm is used to solve it. A shared calibration value is selected according to the preset rules as the common final target calibration value for all experimental conditions that generate the overlapping regions. The single set of target calibration values ​​corresponding to the calibration regions that do not overlap are directly used as the final target calibration values ​​under the corresponding experimental conditions; S4: Write the calibration values ​​of each final target into the control logic of the corresponding test conditions in the ECU program to complete the calibration value update.

[0007] It should be further noted that in step S1, the test conditions include environmental pressure, environmental temperature, and operating mode; Environmental pressure includes standard atmospheric pressure and high-altitude low-pressure environment; Ambient temperature includes normal temperature, extremely cold ambient temperature, and extremely hot ambient temperature; Vehicle operating status includes normal, thermal management, passive regeneration, driving regeneration, and parking regeneration.

[0008] It should be further explained that, in step S1, setting different test conditions to perform the diesel engine DPF carbon loading test specifically involves: Simulate different test conditions in a bench test environment, or control the diesel engine to run for a specified time under steady-state or transient conditions in a real road environment with different test conditions.

[0009] It should be further explained that in step S1, the actual carbon weight gain measurement value is the difference between the carbon weight gain measurement value after performing the diesel engine DPF carbon loading test and the initial carbon weight measurement value before performing the diesel engine DPF carbon loading test. The carbon weight gain measurement value and the initial carbon weight measurement value are obtained by weighing method or pressure difference method.

[0010] It should be further noted that in step S1, the target accuracy is set within the range of 1%-15%.

[0011] It should be further explained that, in step S2, determining the target calibration quantity from the ECU program based on the test conditions of the test data specifically includes: determining the test category based on the environmental pressure and environmental temperature in the set of test data, determining the test mode based on the working mode, and determining the corresponding target calibration quantity from the calibration quantity mapping relationship of the ECU program based on the combination of test category and test mode.

[0012] It should be further noted that in step S2, the carbon weight gain error is calculated using the following formula:

[0013] in, Indicates the error in carbon weight gain; This indicates an increase in the carbon content of the model; This represents the actual carbon weight gain measurement.

[0014] It should be further explained that in step S2, when the absolute value of the carbon weight gain error is less than or equal to the target accuracy, the calibration area is determined based on the current target calibration amount, which includes: taking the current target calibration amount as the center and expanding to both sides according to a preset ratio to form a calibration area.

[0015] It should be further noted that step S2, which involves determining the calibration region and iteratively updating the target calibration value based on the kernel density estimation algorithm, includes: Calculate the kernel density estimate of historical calibration data to obtain the probability density distribution; The region with the highest probability density is selected as the calibration region for the current iteration; The initial update step of the target calibration value is calculated based on the range of the current calibration area; Adjust the target calibration value using the initial update stride; The new carbon weight gain error is calculated based on the adjusted target calibration value. The update step size for the next iteration is dynamically adjusted based on the comparison between the new carbon weight gain error and the historical error. Then, the value of the target calibration value is further adjusted using the adjusted update step size. The rule for dynamically adjusting the update step size is: decrease the update step size when the error signs are opposite, otherwise maintain the current update step size. Repeat the above adjustment process within the preset maximum number of iterations until the new carbon weight gain error meets the target accuracy requirements.

[0016] It should be further explained that step S3, which involves constructing an optimization problem based on the overlapping region and solving it using the NSGA-II multi-objective optimization algorithm, specifically includes: The shared standardization quantity is used as the decision variable, and the extreme values ​​of the overlapping region standardization quantity range are used as the constraints of the decision variable. The objective function is to minimize the sum of the absolute values ​​of errors of each group of experimental data under a shared calibration quantification. Initialize the population and randomly generate candidate solutions with shared calibration within the constraints; Perform non-dominated sorting to divide candidate solutions into multiple frontier levels according to their dominance relationships; Crowding is calculated to assess the distribution density of solutions within the same frontier level; Offspring populations are generated through selection, crossover, and mutation operations; Merge parent and offspring populations and implement an elite preservation strategy; Iterate the above process until the termination condition is met, and output the Pareto optimal solution set.

[0017] It should be further noted that the preset rules include one of the following conditions: Select the solution from the Pareto optimal solution set that minimizes the difference in the absolute value of the error among the experimental data sets; Select the solution from the Pareto optimal solution set that minimizes the absolute value of the error under specific experimental conditions; Based on engineering requirements, higher weights are assigned to the errors of specific test conditions, and the solution with the smallest weighted error is selected.

[0018] It should be further explained that in step S3, when there is overlap in the calibration areas, the single-set data calibration process of step S2 is rerun with a looser precision, and the overlapping areas are determined based on the new single-set data calibration results. Specifically, this includes: For each set of test data that generates the overlapping region, adopt the relaxed accuracy as the new target accuracy, and re-execute the iterative update process in step S2 to obtain the new calibration region corresponding to each set of test data when the relaxed accuracy requirement is met. The overlapping areas between the newly calibrated regions are determined as the final overlapping areas used to construct the subsequent optimization problem; Among them, the slack precision is greater than the original target precision.

[0019] It should be further noted that the range for setting the lenient precision is 15%-20%.

[0020] Secondly, this application provides a calibration update system for a diesel engine DPF carbon load calculation model, used to implement the above-mentioned calibration update method, including: The carbon loading test module is used to set different test conditions to perform diesel engine DPF carbon loading tests, obtain multiple sets of test data, and set target accuracy. The single-set data calibration module is used to perform single-set data calibration for each set of experimental data, including: Based on the test conditions of this set of test data, the target calibration quantity is determined from the ECU program; The test conditions and test data are input into the preset diesel engine DPF carbon load calculation model. By calling the control logic in the ECU program, the carbon weight gain of the model under the target calibration is calculated. The carbon weight gain error between the calculated carbon weight gain and the actual carbon weight gain; Determine whether the absolute value of the carbon weight gain error is less than or equal to the target accuracy. If it is, output the target calibration amount and calibration region for the single set of experimental data. Otherwise, determine the calibration region based on the kernel density estimation algorithm and iteratively update the target calibration amount until the error meets the requirements, and obtain the target calibration amount and calibration region for the single set of experimental data. A multi-set data calibration module is used to perform multi-set data calibration, including: Determine whether the calibration regions of each group of experimental data overlap; When the calibration regions overlap, an optimization problem is constructed based on the overlapping regions, and the NSGA-II multi-objective optimization algorithm is used to solve it. A shared calibration value is selected according to the preset rules as the common final target calibration value for all experimental conditions that generate the overlapping regions. The single set of target calibration values ​​corresponding to the calibration regions that do not overlap are directly used as the final target calibration values ​​under the corresponding experimental conditions; The final target calibration quantity writing module is used to write each final target calibration quantity into the control logic of the corresponding test conditions in the ECU program, thus completing the calibration quantity update.

[0021] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described calibrated update method.

[0022] Fourthly, this application provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described calibrated update method.

[0023] As can be seen from the above technical solutions, this application has the following advantages: 1. This application achieves automated calibration by performing DPF carbon loading tests on diesel engines under different test conditions, obtaining multiple sets of test data and setting target accuracy, and then calibrating each set of test data individually. The target calibration value is iteratively updated based on the kernel density estimation algorithm until the error meets the requirements. This significantly improves calibration efficiency, reduces manual intervention and dependence, and ensures the consistency and repeatability of calibration results.

[0024] 2. This application uses a kernel density estimation algorithm to determine the calibration region and iteratively update the target calibration value, which can quickly converge to a calibration value that meets the target accuracy. This solves the problems of randomness and insufficient accuracy commonly found in manual adjustment and improves the accuracy and reliability of single-set data calibration.

[0025] 3. In this application, multiple sets of experimental data have overlapping calibration regions. By constructing an optimization problem and using the NSGA-II multi-objective optimization algorithm to solve the shared calibration data, the conflict under different experimental conditions is effectively handled, the optimal balance under multiple objectives is achieved, and the applicability and overall accuracy of the model in diverse environments are improved. Attached Figure Description

[0026] To more clearly illustrate the technical solution of this application, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a flowchart of a calibration update method for a diesel engine DPF carbon load calculation model in one embodiment of this application.

[0028] Figure 2 This is a schematic block diagram of a calibration update system for a diesel engine DPF carbon load calculation model in one embodiment of this application.

[0029] Figure 3 This is a schematic diagram of the hardware structure of an electronic device in one embodiment of this application. Detailed Implementation

[0030] To make the purpose, features, and advantages of this application more apparent and understandable, specific embodiments and accompanying drawings will be used to clearly and completely describe the technical solution protected by this application. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0031] The scalar update method involved in this application will be described in detail below. Specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application can also be implemented in other embodiments without these specific details.

[0032] In the standardization update method involved in this application, the term "comprising" indicates the presence of the described feature, integral, step, operation, element, and / or component, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or sets thereof. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0033] To facilitate a clear description of the technical solutions of this application, the terms "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that the terms "first" and "second" do not necessarily imply that they are different.

[0034] The terms "one embodiment" or "some embodiments" used in this application mean that one or more embodiments of this application include the specific features, structures, or characteristics described in that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this application do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized.

[0035] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0036] The calibration update method provided in this application embodiment is executed by a computer device, and correspondingly, the calibration update system of the diesel engine DPF carbon load calculation model runs in the computer device.

[0037] Figure 1 This is a flowchart of a calibration update method for a diesel engine DPF carbon load calculation model according to an embodiment of this application. Figure 1 The implementing entity can be a scalar update system. Depending on different needs, the order of steps in this flowchart can be changed, and some steps can be omitted.

[0038] like Figure 1 As shown, the calibration update method for the diesel engine DPF carbon load calculation model includes: Step S1: Set different test conditions to perform diesel engine DPF carbon loading test, obtain multiple sets of test data, and set target accuracy; The experimental data are actual carbon weight gain measurements.

[0039] By setting various experimental conditions to perform carbon loading experiments and obtaining multiple sets of actual carbon weight gain data, and by pre-setting clear target accuracy, a comprehensive, standardized, and clearly defined input data foundation was provided for all subsequent calibration steps, ensuring the scientific nature of the starting point and the evaluability of the results of the entire calibration process.

[0040] In some specific embodiments, the test conditions include ambient pressure, ambient temperature, and operating mode; Environmental pressure includes standard atmospheric pressure and high-altitude low-pressure environment; Ambient temperature includes normal temperature, extremely cold ambient temperature, and extremely hot ambient temperature; Vehicle operating status includes normal, thermal management, passive regeneration, driving regeneration, and parking regeneration.

[0041] By specifically defining the test conditions as environmental pressure, environmental temperature, and operating mode, and clearly distinguishing between standard atmospheric pressure and high-altitude low-pressure environments, and between ambient temperature and normal temperature, cold and high-temperature environments, the test conditions can comprehensively cover the typical operating conditions of the engine in actual operation, ensuring the representativeness of the obtained test data and the engineering applicability of the calibration results.

[0042] In some specific embodiments, different test conditions are set to perform the diesel engine DPF carbon loading test as follows: Simulate different test conditions in a bench test environment, or control the diesel engine to run for a specified time under steady-state or transient conditions in a real road environment with different test conditions.

[0043] By clarifying that the diesel engine DPF carbon loading test can be performed in a simulated bench test environment or an actual road environment, and that the diesel engine can be controlled to operate under steady-state or transient conditions, a flexible and practical test implementation method is provided, ensuring the availability of test data and the wide applicability of calibration methods.

[0044] In some specific embodiments, the actual carbon weight gain measurement value is the difference between the carbon weight gain measurement value after performing the diesel engine DPF carbon loading test and the initial carbon weight measurement value before performing the diesel engine DPF carbon loading test. The carbon weight gain measurement value and the initial carbon weight measurement value are obtained by weighing method or pressure difference method.

[0045] By defining the actual carbon weight gain measurement value as the difference between the carbon weight measurements before and after the experiment, and clarifying that carbon weight measurement can be obtained by weighing or differential pressure method, a standardized method for acquiring basic data was established, ensuring the accuracy and reliability of the input data and providing a reliable benchmark for the entire calibration process.

[0046] In some specific embodiments, the target accuracy is set in the range of 1%-15%.

[0047] By limiting the target accuracy setting range to 1%-15%, a clear numerical boundary is provided for the selection of target accuracy, ensuring that the calibration process achieves a reasonable balance between accuracy requirements and implementation difficulty, and avoiding resource waste or poor calibration results caused by improper accuracy settings.

[0048] Step S2 involves calibrating each set of experimental data individually, including: Based on the test conditions of this set of test data, the target calibration quantity is determined from the ECU program; The test conditions and test data are input into the preset diesel engine DPF carbon load calculation model. By calling the control logic in the ECU program, the carbon weight gain of the model under the target calibration is calculated. The carbon weight gain error between the calculated carbon weight gain and the actual carbon weight gain; Determine whether the absolute value of the carbon weight gain error is less than or equal to the target accuracy. If it is, output the target calibration amount and calibration region for the single set of experimental data. Otherwise, determine the calibration region based on the kernel density estimation algorithm and iteratively update the target calibration amount until the error meets the requirements, and obtain the target calibration amount and calibration region for the single set of experimental data.

[0049] By independently executing a complete process for each set of experimental data, including determination of calibration quantitation, model calculation, error assessment, and determination and iterative updating of calibration region based on kernel density estimation when accuracy is not met, automated and accurate calibration for a single experimental scenario is achieved. This effectively avoids the subjectivity and randomness of manual adjustments and significantly improves the accuracy and efficiency of single-point calibration.

[0050] In some specific embodiments, determining the target calibration quantity from the ECU program based on the test conditions of the test data specifically includes: determining the test category based on the environmental pressure and environmental temperature in the set of test data, determining the test mode based on the working mode, and determining the corresponding target calibration quantity from the calibration quantity mapping relationship of the ECU program based on the combination of the test category and the test mode.

[0051] By determining the test category based on environmental pressure and temperature, and the test mode based on the operating mode, and then determining the target calibration quantity from the calibration quantity mapping relationship of the ECU program based on this combination, a logically clear calibration quantity positioning mechanism was established, which ensures that the key control parameters that need to be adjusted can be quickly and accurately identified for different test scenarios.

[0052] In some specific embodiments, the carbon weight gain error is calculated using the following formula:

[0053] in, Indicates the error in carbon weight gain; This indicates an increase in the carbon content of the model; This represents the actual carbon weight gain measurement.

[0054] By using a specific relative error percentage formula to calculate the carbon weight gain error, a unified and quantitative mathematical standard is provided for model accuracy evaluation, making the error judgment process more objective and consistent, and providing a clear convergence basis for the iterative update process.

[0055] In some specific embodiments, when the absolute value of the carbon weight gain error is less than or equal to the target accuracy, determining the calibration area based on the current target calibration amount includes: taking the current target calibration amount as the center and expanding to both sides according to a preset ratio to form a calibration area.

[0056] By expanding the calibration area to both sides of the current target calibration value at a preset ratio when the error meets the requirements, an acceptable range of calibration value fluctuation is provided for this set of data, which enhances the robustness of the single-point calibration results and creates conditions for coordinated optimization among multiple sets of data in the future.

[0057] In some specific embodiments, determining the calibration region and iteratively updating the target calibration value based on the kernel density estimation algorithm includes: Calculate the kernel density estimate of historical calibration data to obtain the probability density distribution; The region with the highest probability density is selected as the calibration region for the current iteration; The initial update step of the target calibration value is calculated based on the range of the current calibration area; Adjust the target calibration value using the initial update stride; The new carbon weight gain error is calculated based on the adjusted target calibration value. The update step size for the next iteration is dynamically adjusted based on the comparison between the new carbon weight gain error and the historical error. Then, the value of the target calibration value is further adjusted using the adjusted update step size. The rule for dynamically adjusting the update step size is: decrease the update step size when the error signs are opposite, otherwise maintain the current update step size. Repeat the above adjustment process within the preset maximum number of iterations until the new carbon weight gain error meets the target accuracy requirements.

[0058] By determining the calibration region based on kernel density estimation and dynamically adjusting the update step size according to the change of error sign, a data-driven intelligent iterative strategy is realized, which not only ensures the convergence speed but also effectively prevents oscillations, thereby improving the stability and efficiency of the calibration process.

[0059] Step S3 involves calibrating multiple sets of data, including: Determine whether the calibration regions of each group of experimental data overlap; When the calibration regions overlap, an optimization problem is constructed based on the overlapping regions, and the NSGA-II multi-objective optimization algorithm is used to solve it. A shared calibration value is selected according to the preset rules as the common final target calibration value for all experimental conditions that generate the overlapping regions. The target calibration value of a single set of data corresponding to a calibration region that does not overlap is directly used as the final target calibration value under the corresponding experimental conditions.

[0060] By analyzing the overlap of calibration regions in multiple sets of experimental data and constructing a multi-objective optimization problem for the overlapping regions, the NSGA-II algorithm is used to solve the shared calibration quantity. This achieves global optimization calibration under various experimental constraints, ensuring the wide adaptability of the single calibration quantity under complex working conditions and optimal overall accuracy.

[0061] In some specific embodiments, constructing an optimization problem based on overlapping regions and solving it using the NSGA-II multi-objective optimization algorithm specifically includes: The shared standardization quantity is used as the decision variable, and the extreme values ​​of the overlapping region standardization quantity range are used as the constraints of the decision variable. The objective function is to minimize the sum of the absolute values ​​of errors of each group of experimental data under a shared calibration quantification. Initialize the population and randomly generate candidate solutions with shared calibration within the constraints; Perform non-dominated sorting to divide candidate solutions into multiple frontier levels according to their dominance relationships; Crowding is calculated to assess the distribution density of solutions within the same frontier level; Offspring populations are generated through selection, crossover, and mutation operations; Merge parent and offspring populations and implement an elite preservation strategy; Iterate the above process until the termination condition is met, and output the Pareto optimal solution set.

[0062] By employing the NSGA-II multi-objective optimization algorithm, with the extreme values ​​of the overlapping regions as constraints and minimizing the sum of the absolute values ​​of the errors of each group as the objective, a systematic multi-objective optimization method is provided. This method can effectively find the Pareto solution set that optimizes the overall accuracy of each group of experimental data under complex constraints.

[0063] In some specific embodiments, the preset rules include one of the following conditions: Select the solution from the Pareto optimal solution set that minimizes the difference in the absolute value of the error among the experimental data sets; Select the solution from the Pareto optimal solution set that minimizes the absolute value of the error under specific experimental conditions; Based on engineering requirements, higher weights are assigned to the errors of specific test conditions, and the solution with the smallest weighted error is selected.

[0064] By pre-setting multiple specific rules for selecting the final shared calibration quantifier from the Pareto optimal solution set, a flexible decision-making basis is provided for engineering applications, enabling the calibration results to be selected in a targeted manner according to the priority requirements of different application scenarios, thereby enhancing the practicality and adaptability of the method.

[0065] In some specific embodiments, when the calibration areas overlap, the single-set data calibration process in step S2 is rerun with a looser precision, and the overlapping areas are determined based on the new single-set data calibration results. Specifically, this includes: For each set of test data that generates the overlapping region, adopt the relaxed accuracy as the new target accuracy, and re-execute the iterative update process in step S2 to obtain the new calibration region corresponding to each set of test data when the relaxed accuracy requirement is met. The overlapping areas between the newly calibrated regions are determined as the final overlapping areas used to construct the subsequent optimization problem; Among them, the slack precision is greater than the original target precision.

[0066] By re-performing single-set data calibration with relaxed precision when calibration regions overlap to determine new overlapping regions, and by appropriately relaxing the single-point precision requirements to promote regional overlap, a larger feasible space is provided for multi-objective optimization solutions, and the success rate of finding shared calibration quantities is improved.

[0067] In some specific embodiments, the loose precision setting ranges from 15% to 20%.

[0068] By limiting the range of relaxed precision settings to 15%-20%, clear numerical guidance is provided for the relaxation of precision during the recalibration process, ensuring that while promoting regional overlap, the calibration accuracy of individual data sets remains within a reasonable engineering acceptable range.

[0069] Step S4: Write the calibration values ​​of each final target into the control logic of the corresponding test conditions in the ECU program to complete the calibration value update.

[0070] By writing the calibration values ​​of each final target into the control logic of the corresponding test conditions in the ECU program, the final application of the calibration results was completed, enabling the optimized carbon load calculation model to directly serve the actual control of the engine, thus achieving a seamless connection from the calibration process to engineering application.

[0071] The following are embodiments of the calibration update system for the diesel engine DPF carbon load calculation model provided in this application. This calibration update system for the diesel engine DPF carbon load calculation model belongs to the same inventive concept as the calibration update methods in the above embodiments. For details not described in the embodiments of the calibration update system, please refer to the embodiments of the calibration update method for the diesel engine DPF carbon load calculation model described above.

[0072] like Figure 2 As shown, the calibration update system for the diesel engine DPF carbon load calculation model includes: The carbon loading test module is used to set different test conditions to perform diesel engine DPF carbon loading tests, obtain multiple sets of test data, and set target accuracy. The single-set data calibration module is used to perform single-set data calibration for each set of experimental data, including: Based on the test conditions of this set of test data, the target calibration quantity is determined from the ECU program; The test conditions and test data are input into the preset diesel engine DPF carbon load calculation model. By calling the control logic in the ECU program, the carbon weight gain of the model under the target calibration is calculated. The carbon weight gain error between the calculated carbon weight gain and the actual carbon weight gain; Determine whether the absolute value of the carbon weight gain error is less than or equal to the target accuracy. If it is, output the target calibration amount and calibration region for the single set of experimental data. Otherwise, determine the calibration region based on the kernel density estimation algorithm and iteratively update the target calibration amount until the error meets the requirements, and obtain the target calibration amount and calibration region for the single set of experimental data. A multi-set data calibration module is used to perform multi-set data calibration, including: Determine whether the calibration regions of each group of experimental data overlap; When the calibration regions overlap, an optimization problem is constructed based on the overlapping regions, and the NSGA-II multi-objective optimization algorithm is used to solve it. A shared calibration value is selected according to the preset rules as the common final target calibration value for all experimental conditions that generate the overlapping regions. The single set of target calibration values ​​corresponding to the calibration regions that do not overlap are directly used as the final target calibration values ​​under the corresponding experimental conditions; The final target calibration quantity writing module is used to write each final target calibration quantity into the control logic of the corresponding test conditions in the ECU program, thus completing the calibration quantity update.

[0073] The calibration update system in this embodiment is used to implement the calibration update method for the diesel engine DPF carbon load calculation model.

[0074] This application also provides an electronic device for implementing the various embodiments of this application. Figure 3 To illustrate the hardware structure of an electronic device according to various embodiments of this application, as shown in the following diagram... Figure 3 As shown, the electronic device includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor.

[0075] Those skilled in the art will understand that the electronic device structure involved in the embodiments of this application does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0076] In embodiments of this application, electronic devices include, but are not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of this application described and / or claimed herein.

[0077] In this application embodiment, the processor can be implemented using at least one of an Application-Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a processor, a controller, a microcontroller, a microprocessor, or an electronic unit designed to perform the functions described herein. In some cases, such implementations can be implemented within a controller. For software implementations, implementations such as processes or functions can be implemented with separate software modules that allow the performance of at least one function or operation. The software code can be implemented by a software application (or program) written in any suitable programming language, and the software code can be stored in memory and executed by the controller.

[0078] In addition, the electronic device includes some functional modules not shown, which will not be described in detail here.

[0079] Those skilled in the art will understand that the various aspects of the electronic device provided in this application can be implemented as a system, method, or program product. Therefore, the various aspects of this application can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "system."

[0080] This application also provides a storage medium storing a program product capable of implementing a calibration update method for a diesel engine DPF carbon load calculation model. In some possible implementations, various aspects of this application can also be implemented as a program product comprising program code that, when run on a terminal device, causes the terminal device to perform the steps described in the foregoing "Exemplary Methods" section of this specification according to various exemplary embodiments of this application.

[0081] The storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example,, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0082] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for calibrating and updating a diesel engine DPF carbon load calculation model, characterized in that, include: S1: Set different test conditions to perform diesel engine DPF carbon loading test, obtain multiple sets of test data, and set the target accuracy; The experimental data are actual measured values ​​of carbon weight gain; S2: Perform individual data calibration for each set of experimental data, including: Based on the test conditions of this set of test data, the target calibration quantity is determined from the ECU program; The test conditions and test data are input into the preset diesel engine DPF carbon load calculation model. By calling the control logic in the ECU program, the carbon weight gain of the model under the target calibration is calculated. The carbon weight gain error between the calculated carbon weight gain and the actual carbon weight gain; Determine whether the absolute value of the carbon weight gain error is less than or equal to the target accuracy. If it is, output the target calibration amount and calibration region for the single set of experimental data. Otherwise, determine the calibration region based on the kernel density estimation algorithm and iteratively update the target calibration amount until the error meets the requirements, and obtain the target calibration amount and calibration region for the single set of experimental data. S3: Perform multiple sets of data calibration, including: Determine whether the calibration regions of each group of experimental data overlap; When the calibration regions overlap, an optimization problem is constructed based on the overlapping regions, and the NSGA-II multi-objective optimization algorithm is used to solve it. A shared calibration value is selected according to the preset rules as the common final target calibration value for all experimental conditions that generate the overlapping regions. The single set of target calibration values ​​corresponding to the calibration regions that do not overlap are directly used as the final target calibration values ​​under the corresponding experimental conditions; S4: Write the calibration values ​​of each final target into the control logic of the corresponding test conditions in the ECU program to complete the calibration value update.

2. The calibration update method as described in claim 1, characterized in that, In step S1, the test conditions include ambient pressure, ambient temperature, and operating mode; Environmental pressure includes standard atmospheric pressure and high-altitude low-pressure environment; Ambient temperature includes normal temperature, extremely cold ambient temperature, and extremely hot ambient temperature; Vehicle operating status includes normal, thermal management, passive regeneration, driving regeneration, and parking regeneration.

3. The calibration update method as described in claim 1, characterized in that, In step S1, the actual carbon weight gain measurement value is the difference between the carbon weight gain measurement value after performing the diesel engine DPF carbon loading test and the initial carbon weight measurement value before performing the diesel engine DPF carbon loading test. The carbon weight gain measurement value and the initial carbon weight measurement value are obtained by weighing method or pressure difference method.

4. The calibration update method as described in claim 1, characterized in that, In step S2, the carbon weight gain error is calculated using the following formula: in, Indicates the error in carbon weight gain; This indicates an increase in the carbon content of the model; This represents the actual carbon weight gain measurement.

5. The calibration update method as described in claim 1, characterized in that, In step S2, determining the calibration region and iteratively updating the target calibration value based on the kernel density estimation algorithm includes: Calculate the kernel density estimate of historical calibration data to obtain the probability density distribution; The region with the highest probability density is selected as the calibration region for the current iteration; The initial update step of the target calibration value is calculated based on the range of the current calibration area; Adjust the target calibration value using the initial update stride; The new carbon weight gain error is calculated based on the adjusted target calibration value. The update step size for the next iteration is dynamically adjusted based on the comparison between the new carbon weight gain error and the historical error. Then, the value of the target calibration value is further adjusted using the adjusted update step size. The rule for dynamically adjusting the update step size is: decrease the update step size when the error signs are opposite, otherwise maintain the current update step size. Repeat the above adjustment process within the preset maximum number of iterations until the new carbon weight gain error meets the target accuracy requirements.

6. The calibration update method as described in claim 1, characterized in that, In step S3, the optimization problem is constructed based on the overlapping region and solved using the NSGA-II multi-objective optimization algorithm, specifically including: The shared standardization quantity is used as the decision variable, and the extreme values ​​of the overlapping region standardization quantity range are used as the constraints of the decision variable. The objective function is to minimize the sum of the absolute values ​​of errors of each group of experimental data under a shared calibration quantification. Initialize the population and randomly generate candidate solutions with shared calibration within the constraints; Perform non-dominated sorting to divide candidate solutions into multiple frontier levels according to their dominance relationships; Crowding is calculated to assess the distribution density of solutions within the same frontier level; Offspring populations are generated through selection, crossover, and mutation operations; Merge parent and offspring populations and implement an elite preservation strategy; Iterate the above process until the termination condition is met, and output the Pareto optimal solution set.

7. The calibration update method as described in claim 1, characterized in that, The preset rules include one of the following conditions: Select the solution from the Pareto optimal solution set that minimizes the difference in the absolute value of the error among the experimental data sets; Select the solution from the Pareto optimal solution set that minimizes the absolute value of the error under specific experimental conditions; Based on engineering requirements, higher weights are assigned to the errors of specific test conditions, and the solution with the smallest weighted error is selected.

8. A calibration update system for a diesel engine DPF carbon load calculation model, characterized in that, To implement the calibrated update method as described in any one of claims 1-7, comprising: The carbon loading test module is used to set different test conditions to perform diesel engine DPF carbon loading tests, obtain multiple sets of test data, and set target accuracy. The single-set data calibration module is used to perform single-set data calibration for each set of experimental data, including: Based on the test conditions of this set of test data, the target calibration quantity is determined from the ECU program; The test conditions and test data are input into the preset diesel engine DPF carbon load calculation model. By calling the control logic in the ECU program, the carbon weight gain of the model under the target calibration is calculated. The carbon weight gain error between the calculated carbon weight gain and the actual carbon weight gain; Determine whether the absolute value of the carbon weight gain error is less than or equal to the target accuracy. If it is, output the target calibration amount and calibration region for the single set of experimental data. Otherwise, determine the calibration region based on the kernel density estimation algorithm and iteratively update the target calibration amount until the error meets the requirements, and obtain the target calibration amount and calibration region for the single set of experimental data. A multi-set data calibration module is used to perform multi-set data calibration, including: Determine whether the calibration regions of each group of experimental data overlap; When the calibration regions overlap, an optimization problem is constructed based on the overlapping regions, and the NSGA-II multi-objective optimization algorithm is used to solve it. A shared calibration value is selected according to the preset rules as the common final target calibration value for all experimental conditions that generate the overlapping regions. The single set of target calibration values ​​corresponding to the calibration regions that do not overlap are directly used as the final target calibration values ​​under the corresponding experimental conditions; The final target calibration quantity writing module is used to write each final target calibration quantity into the control logic of the corresponding test conditions in the ECU program, thus completing the calibration quantity update.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor is used to execute a computer program, it implements the steps of the calibrated update method as described in any one of claims 1-7.

10. A storage medium storing a computer program, characterized in that, When a computer program is executed by a processor, it implements the steps of the calibrated update method as described in any one of claims 1-7.

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