Method and system for calibrating transient working condition exhaust temperature of virtual engine model based on AVLCruiseM building

Abstract

The invention discloses a method and system for calibrating the transient working condition exhaust temperature of a virtual engine model based on AVLCruiseM construction, and relates to the field of simulation calculation, and the method comprises the steps: obtaining engine physical structure parameters and performance test data, and carrying out basic engine model construction and steady-state model calibration processing to obtain a steady-state calibration model; performing transient condition decomposition presetting, constructing a step heat exchange control model and a preliminary sensor control model according to a condition classification result, performing simulation exhaust temperature precision judgment, and if the precision does not meet a preset requirement, performing adjustment processing according to a difference between transient test data and a simulation result to obtain an optimized transient model; fixing a code module, an interface and a signal flow to obtain a calibrated transient engine model; and integrating to a virtual development platform for processing to obtain a virtual engine system. The method is used for solving the problem that in the prior art, the exhaust temperature prediction deviation of a steady-state model under the transient working condition is large.

Description

Technical Field

[0001] This invention relates to the field of simulation computing technology, and in particular to a method and system based on AVLCruiseM for calibrating the exhaust temperature of a virtual engine model under transient operating conditions. Background Technology

[0002] In the development and use of modern engines, virtual calibration or calibration based on simulation models is no longer just an auxiliary tool, but has become a key driving force for shortening development cycles, reducing costs, and improving engine performance. The virtual engine model developed based on AVLCruiseM plays a key role in virtual calibration technology as a "system integration and vehicle environment simulator". Its function can be summarized as: acting as a "digital substitute" for the engine, building a safe, efficient, and infinitely replicable virtual experimental environment in the computer, realizing a fundamental transformation of engine calibration from "trial and error" to "model-driven".

[0003] In modern engine development, due to the diverse real-world application scenarios of vehicles, engines not only need to meet normal bench calibration requirements but also need to consider various transient road patterns. Exhaust temperature is a crucial indicator of engine operating status, especially under transient conditions such as start-up, acceleration, and deceleration. Rapid changes in exhaust temperature reflect complex internal physical processes within the engine, including combustion, heat transfer, and gas flow. Accurately understanding transient exhaust temperature is essential for optimizing engine combustion processes, designing efficient thermal management systems, reducing emissions, and improving engine durability and reliability. Current transient models... During development, models are often fitted based on steady-state tests and directly applied to transient operating conditions. However, due to thermal inertia, heat transfer delay, and dynamic changes in airflow and boundary conditions, the exhaust temperature variation of the steady-state engine model under transient operating conditions deviates significantly from the actual test results. For example, because engine components (such as cylinder walls and exhaust manifolds) have thermal inertia, the steady-state model assumes that their temperature is in real-time equilibrium with the operating conditions. However, during transient processes, component temperature changes lag behind changes in operating conditions. The heat transfer rate between high-temperature combustion gases and low-temperature components is much higher than in steady-state conditions, and more heat is absorbed by the components, resulting in exhaust temperatures that are much lower than the actual test results. Consequently, the transient model has poor accuracy in predicting exhaust temperature.

[0004] This patent is based on AVLCruiseM to build a calibration method and system for exhaust temperature under transient operating conditions of virtual engine models. It can solve the problem that the exhaust temperature variation law under transient operating conditions of the aforementioned methods deviates greatly from the actual test results, and can significantly improve the accuracy of exhaust temperature of virtual engine transient models, effectively improve development results and shorten the development cycle.

[0005] The information disclosed in this background section is intended only to enhance the understanding of the overall background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0006] This invention provides a method and system for calibrating the exhaust temperature of a virtual engine model under transient operating conditions based on AVLCruiseM, in order to solve the problem of large prediction deviation of exhaust temperature under transient operating conditions in the existing steady-state model;

[0007] To achieve the above objectives, this invention provides a method for calibrating the exhaust temperature of a virtual engine model under transient operating conditions based on AVLCruiseM, comprising:

[0008] Obtain engine physical structure parameters and performance test data, perform data preprocessing, and obtain the basic dataset;

[0009] Based on the aforementioned basic dataset, a basic engine model is constructed and a steady-state model is calibrated to obtain the calibrated steady-state model.

[0010] Based on the steady-state calibration model, a transient operating condition decomposition preset is performed, and the operating conditions are divided into idling, reverse dragging and normal ignition operating conditions to obtain the operating condition classification results.

[0011] Based on the operating condition classification results, the exhaust temperature heat transfer model interface is modified and signal streams are added, and code modules are associated to control the heat transfer process between gas and solid, and between solid and environment, to obtain a preliminary heat transfer control model.

[0012] Based on the operating condition classification results, the exhaust temperature sensor model interface was modified and a signal stream was added. The associated code module was used to control the heat exchange process between the gas and the probe, thus obtaining a preliminary sensor control model.

[0013] Based on the preliminary heat exchange control model and the preliminary sensor control model, the accuracy of the simulated exhaust temperature is judged. If the accuracy does not meet the preset requirements, adjustments are made based on the difference between the transient test data and the simulation results to obtain the optimized transient model.

[0014] Based on the optimized transient model, the code modules, interfaces, and signal flows are fixed to obtain the calibrated transient engine model.

[0015] Based on the calibrated transient engine model, it is integrated into the virtual development platform to obtain a virtual engine system that can be accurately controlled in an online environment.

[0016] In one embodiment of the present invention, based on the basic dataset, a basic engine model is constructed and a steady-state model is calibrated to obtain a calibrated steady-state model, including:

[0017] Based on the engine structure data in the aforementioned basic dataset, a basic engine model is constructed using the MOBEOCylinder class model.

[0018] Based on the experimental data in the aforementioned basic dataset, the engine steady-state operating condition is calibrated, including the optimization of combustion efficiency and heat transfer parameters, to obtain a preliminary steady-state model.

[0019] Based on the basic engine model and the preliminary steady-state model, and according to the steady-state accuracy verification requirements, the model output and experimental data are compared and analyzed to obtain the calibrated steady-state calibration model.

[0020] In one embodiment of the present invention, based on the steady-state calibration model, a transient operating condition decomposition is preset, dividing the operating conditions into idling, reverse dragging, and normal ignition conditions, to obtain the operating condition classification results, including:

[0021] Based on the output characteristics of the steady-state calibration model, transient road spectrum analysis is performed to obtain the operating condition variation law;

[0022] Based on the changing patterns of operating conditions and using the cyclic fuel injection quantity as the logical judgment boundary, the operating conditions are classified. The idling condition is defined as the fuel injection quantity being lower than the threshold, the reverse dragging condition is defined as the fuel injection quantity being zero, and the normal ignition condition is defined as the fuel injection quantity being normal, thus obtaining the operating condition classification results.

[0023] In one embodiment of the present invention, based on the operating condition classification results, the exhaust temperature heat transfer model interface is modified and a signal stream is added, and a code module is associated to control the heat transfer process between gas and solid, and between solid and the environment, to obtain a preliminary heat transfer control model, including:

[0024] Based on the operating condition classification results, the exhaust temperature heat transfer model interface in the AVLCruiseM software was modified to obtain the updated interface structure.

[0025] Based on the requirements of the updated interface, signal flow addition processing is performed, including the input of electronic control data oil quantity signal and the output of heat transfer coefficient signal, to obtain the signal flow network;

[0026] Based on the operating condition classification results and signal flow network, code modules are programmed to control the heat transfer coefficient setting between gas and solid, thus obtaining a gas-solid heat transfer control module.

[0027] Based on the operating condition classification results and signal flow network, the code module is expanded to control the setting of the heat transfer coefficient between the solid and the environment, including setting the adiabatic conditions under idling conditions, to obtain a preliminary heat transfer control model.

[0028] In one embodiment of the present invention, based on the operating condition classification results, the exhaust temperature sensor model interface is modified and a signal stream is added, and a code module is associated to control the heat exchange process between the gas and the probe, resulting in a preliminary sensor control model, including:

[0029] Based on the operating condition classification results, the exhaust temperature sensor model interface is modified to obtain the sensor interface update structure;

[0030] Based on the update requirements of the sensor interface update structure, signal flow addition processing is performed, including the transmission of heat exchange coefficient signals, to obtain the sensor signal flow network;

[0031] Based on the working condition classification results and the sensor signal flow network, code modules are programmed to control the convective heat transfer and radiative heat transfer coefficients between the gas and the probe, thus obtaining the sensor heat exchange control module.

[0032] Based on the sensor signal flow network required for sensor thermal inertia simulation, the code module is optimized to adjust the heat transfer factor under different operating conditions, thus obtaining a preliminary sensor control model.

[0033] In one embodiment of the present invention, based on the preliminary heat transfer control model and the preliminary sensor control model, the accuracy of the simulated exhaust temperature is judged. If the accuracy does not meet the preset requirements, adjustments are made based on the difference between the transient test data and the simulation results to obtain an optimized transient model, including:

[0034] Based on the integrated output of the preliminary heat exchange control model and the preliminary sensor control model, the exhaust temperature is calculated to obtain the initial simulation results;

[0035] Based on the preset accuracy threshold and the initial simulation results, the simulation results are compared and analyzed with the transient test data to obtain the accuracy deviation data;

[0036] Based on the accuracy deviation data, the calibration parameters in the code module are iteratively adjusted, including the optimization of heat transfer coefficient and heat exchange coefficient, to obtain the adjusted control module;

[0037] The adjusted control module was iteratively verified multiple times, and the model accuracy was re-evaluated until the simulated exhaust temperature met the preset requirements, thus obtaining the optimized transient model.

[0038] In one embodiment of the present invention, based on the optimized transient model, code modules, interfaces, and signal flows are fixed to obtain a calibrated transient engine model, including:

[0039] Based on the stability verification of the optimized transient model, the parameters of the code module are solidified to obtain a fixed code module;

[0040] Based on the fixed code module and interface compatibility requirements, interface and signal flow locking is performed to obtain a fixed interface structure;

[0041] Based on the fixed interface structure and model portability requirements, the overall structure is saved and encapsulated to obtain the calibrated transient engine model.

[0042] On the other hand, a system based on AVLCruiseM for calibrating the exhaust temperature of a virtual engine model under transient operating conditions includes:

[0043] The acquisition module is used to acquire engine physical structure parameters and performance test data, perform data preprocessing, and obtain a basic dataset.

[0044] The processing module is used to construct a basic engine model and calibrate a steady-state model based on the aforementioned basic dataset, obtaining a calibrated steady-state model. Based on the steady-state calibration model, it performs transient condition decomposition pre-setting, classifying the conditions into idling, reverse drag, and normal ignition conditions, obtaining condition classification results. Based on the condition classification results, it modifies the exhaust temperature heat transfer model interface and adds signal streams, and associates code modules to control the heat transfer process between gas and solid, and between solid and the environment, obtaining a preliminary heat transfer control model. Based on the condition classification results, it modifies the exhaust temperature sensor model interface and adds signal streams, and associates code modules to... The heat exchange process between the controlled gas and the probe is used to obtain a preliminary sensor control model. Based on the preliminary heat exchange control model and the preliminary sensor control model, the accuracy of the simulated exhaust temperature is judged. If the accuracy does not meet the preset requirements, adjustments are made based on the difference between the transient test data and the simulation results to obtain an optimized transient model. Based on the optimized transient model, the code modules, interfaces, and signal flows are fixed to obtain a calibrated transient engine model. Based on the calibrated transient engine model, it is integrated into a virtual development platform to obtain a virtual engine system that can be accurately controlled in an online environment.

[0045] On the other hand, the present invention also 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 program to implement the method for calibrating the exhaust temperature of a virtual engine model under transient operating conditions based on AVLCruiseM as described above.

[0046] On the other hand, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method for calibrating the exhaust temperature of a virtual engine model under transient operating conditions based on AVLCruiseM as described above.

[0047] On the other hand, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the method for calibrating the exhaust temperature of a virtual engine model under transient operating conditions based on AVLCruiseM as described above.

[0048] Compared with the prior art, the present invention provides a method and system for calibrating the exhaust temperature of a virtual engine model under transient operating conditions based on AVLCruiseM. Through sub-condition modeling and dynamic parameter adjustment, the present invention can accurately simulate the exhaust temperature changes of the engine under transient operating conditions such as acceleration, deceleration, and idling. The simulation results are in much better agreement with the real test data than traditional steady-state models or unverified simple transient models.

[0049] It effectively shortens the development cycle of engine electronic control system, reduces development costs, and provides a high-precision "virtual engine" alternative, enabling engineers to conduct a large amount of control strategy testing and calibration work in a software environment (MIL, Model in the Loop), reducing reliance on expensive and time-constrained engine bench tests. This not only moves some development work forward and shortens the overall cycle, but also significantly reduces manpower, material resources and time costs.

[0050] Enhancing the predictive and control capabilities of thermal management in aftertreatment systems is crucial, as exhaust temperature is a key factor affecting the efficiency and lifespan of aftertreatment devices such as SCR (Selective Catalytic Reduction) and DPF (Diesel Particulate Filter). This model can accurately predict changes in exhaust temperature under transient operating conditions, providing key data support for optimizing thermal management control strategies of aftertreatment systems (such as fuel injection and throttle control). This helps ensure that aftertreatment systems can operate efficiently and stably under various driving conditions, thereby reducing emissions.

[0051] To improve the practicality and integration convenience of the model, the final generated model is a standardized module that has been solidified and packaged, with good stability and portability. It can be easily integrated into the whole vehicle virtual development platform for more complex system-level simulations such as whole vehicle energy management and emission prediction, thus expanding the application scenarios of the model.

[0052] To provide a methodological reference for solving similar complex system modeling problems, the technical route of "operating condition decomposition → interface / signal flow reconstruction → modular coding → closed-loop iterative calibration" adopted in this invention is not only applicable to engine exhaust temperature modeling, but its ideas and methodology can also be transferred to the modeling of other complex physical systems with multimodal, nonlinear, and strong transient characteristics, and has broad reference value. Attached Figure Description

[0053] Figure 1 This is a flowchart illustrating a method for calibrating the exhaust temperature of a virtual engine model under transient operating conditions based on AVLCruiseM according to an embodiment of the present invention.

[0054] Figure 2 This is a schematic diagram of a system for calibrating the exhaust temperature of a virtual engine model under transient operating conditions, based on AVLCruiseM according to an embodiment of the present invention.

[0055] Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0056] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings, but it should be understood that the scope of protection of the present invention is not limited to the specific embodiments.

[0057] Unless otherwise expressly stated, throughout the specification and claims, the term "comprising" or its variations such as "including" or "comprises" shall be understood to include the stated elements or components without excluding other elements or other components.

[0058] like Figure 1 As shown in the embodiment of the present invention, the method for calibrating the exhaust temperature of a virtual engine model under transient operating conditions based on AVLCruiseM mainly includes the following steps:

[0059] 11. Obtain engine physical structure parameters and performance test data, perform data preprocessing, and obtain the basic dataset;

[0060] 12. Based on the aforementioned basic dataset, construct a basic engine model and calibrate the steady-state model to obtain the calibrated steady-state model;

[0061] 13. Based on the steady-state calibration model, perform transient condition decomposition preset, divide the conditions into idling, reverse dragging and normal ignition conditions, and obtain the condition classification results;

[0062] 14. Based on the working condition classification results, modify the exhaust temperature heat transfer model interface and add signal streams, and associate code modules to control the heat transfer process between gas and solid, and between solid and environment, to obtain a preliminary heat transfer control model.

[0063] 15. Based on the working condition classification results, modify the exhaust temperature sensor model interface and add signal streams, and associate code modules to control the heat exchange process between the gas and the probe to obtain a preliminary sensor control model.

[0064] 16. Based on the preliminary heat exchange control model and the preliminary sensor control model, the accuracy of the simulated exhaust temperature is judged. If the accuracy does not meet the preset requirements, adjustments are made based on the difference between the transient test data and the simulation results to obtain the optimized transient model.

[0065] 17. Based on the optimized transient model, perform fixed processing on the code modules, interfaces and signal flows to obtain the calibrated transient engine model;

[0066] 18. Based on the calibrated transient engine model, integrate it into the virtual development platform to obtain a virtual engine system that can be accurately controlled in an online environment.

[0067] In this embodiment of the invention, step 11 provides an accurate and consistent data foundation for the entire model construction; by cleaning and standardizing the engine physical parameters and performance test data, noise and errors in the original data are eliminated, ensuring the reliability of the data upon which subsequent model calibration depends, and guaranteeing the accuracy of the model from the source; step 12 establishes a reliable benchmark model under stable operating conditions; this steady-state model acts as an "anchor point," providing the correct physical relationship starting point and parameter foundation for the subsequent extension of the transient model, avoiding the difficulty of "starting from scratch" on complex transient problems; step 13 implements... This approach employs a "divide and conquer" strategy for complex problems. By intelligently dividing the continuous transient process into typical operating conditions such as "idling, reverse driving, and normal ignition" using key parameters like fuel injection quantity, it identifies the essential differences in physical mechanisms (such as heat transfer conditions) under different operating conditions, providing a precise logical basis for subsequent targeted model reconstruction. This represents a strategic innovation for achieving high-precision simulation. Steps 14 and 15 achieve a fundamental leap from "static" to "dynamic" modeling. By modifying interfaces, adding signal flows, and associating dedicated code modules, the model can dynamically adjust the heat transfer coefficient and heat exchange process based on the operating condition determination in step 13. For example... The simulation of near-adiabatic conditions under idling conditions and the consideration of environmental cooling effects under reversing conditions are performed. Step 15 integrates the simulation of the dynamic response (thermal inertia) of the physical sensors, making the model output a "performance temperature" that is closer to the actual sensor readings than the ideal theoretical temperature, greatly improving the comparability and consistency with bench test data. Step 16 forms a "closed loop" for accuracy improvement. By comparing the simulation results with transient test data and repeatedly adjusting the model parameters accordingly, the model is ensured not only to pass the "calibration point" but also to accurately reproduce the changing trend of the entire transient process, ultimately ensuring that the model accuracy meets strict requirements. The engineering application requirements; Step 17 solidifies and encapsulates the optimized model parameters and control logic to form a stable and reliable independent module; This ensures the consistency and reusability of the model under different application scenarios and avoids abnormal simulation results caused by unexpected parameter changes; Step 18 integrates the final model into the virtual development platform, transforming it from an "offline" analysis tool into a "virtual engine" that can be co-simulated with the whole vehicle model in an online environment and used to test the control strategy of the electronic control unit (ECU); This is the final step in realizing value and provides a key component for the model-based V-shaped development process;

[0068] The method of this invention, through a complete process of data preparation, steady-state foundation laying, operating condition decomposition, model reconstruction, closed-loop verification, and solidification integration, ultimately achieves the following core effect: it successfully constructs a virtual model system capable of accurately predicting engine exhaust temperature under transient operating conditions; it changes the situation where traditional steady-state models or simple transient models have large errors in exhaust temperature prediction, and provides an unprecedented reliable platform for the virtual development and testing of thermal management control strategies for engines, especially aftertreatment systems, thus demonstrating great application value in reducing R&D costs, shortening development cycles, and improving control quality.

[0069] like Figure 1 As shown in Figure 12, based on the aforementioned basic dataset, a basic engine model is constructed and a steady-state model is calibrated to obtain the calibrated steady-state model, including:

[0070] 121. Based on the engine structure data in the aforementioned basic dataset, construct a basic engine model based on the MOBEOCylinder class model;

[0071] 122. Based on the experimental data in the aforementioned basic dataset, perform calibration processing for the engine's steady-state operating conditions, including optimization of combustion efficiency and heat transfer parameters, to obtain a preliminary steady-state model;

[0072] 123. Based on the basic engine model and the preliminary steady-state model, and according to the steady-state accuracy verification requirements, the model output and the experimental data are compared and analyzed to obtain the calibrated steady-state calibration model.

[0073] In this embodiment of the invention, step 12 and its sub-steps are the cornerstone and prerequisite for constructing a high-precision transient exhaust temperature model. They create a highly reliable engine model under steady-state conditions from scratch, providing a physically correct and reliable starting point for subsequent extended calibration under transient conditions. Step 121 establishes a correct physical modeling framework, constructing a basic engine model based on the MOBEOCylinder class model, ensuring the physical accuracy and computational efficiency of the model's core architecture. The MOBEO (Model-Based Engine Optimization) model is an industrially validated, mature modeling method based on physical laws. Choosing the MOBEOCylinder class signifies the core of the model... (Such as cylinder working volume, intake and exhaust ports, etc.) are directly derived from the actual physical structure data of the engine (such as cylinder bore, stroke, compression ratio); this avoids the complexity and potential errors of building physical equations from scratch, ensuring that the model has the ability to reflect the basic working law of the engine from the very beginning; this "basic engine model" is a fully functional but parameter-undefined "white box model", laying the correct physical framework for subsequent accurate calibration; step 122 achieves the first calibration between theory and reality, using steady-state test data to optimize key parameters such as combustion efficiency and heat transfer, initially "anchoring" the idealized theoretical model to the real engine behavior; although the "basic engine model" is physically correct, its built-in default parameters (such as combustion...) Combustion efficiency and wall heat transfer coefficient are general values ​​and cannot accurately match the performance of a specific engine. Sub-step 122, the calibration process, addresses this "personalization" issue. By adjusting the combustion efficiency parameters, the power and torque output calculated by the model are made consistent with the experimental data. By optimizing the heat transfer parameters, the cylinder temperature and exhaust temperature calculated by the model are made consistent with the experimental data. The "preliminary steady-state model" obtained in this step is already a specialized model capable of accurately reproducing the engine's performance at multiple stable operating points (such as different speeds and loads). It proves the effectiveness of the model framework and the correct direction of parameter optimization. Step 123 completes the rigorous verification of the model's accuracy. Through systematic comparative analysis, the preliminary calibration results are quantitatively verified to ensure the model's accuracy. The accuracy under steady-state conditions fully meets engineering application standards. This is not just a simple data comparison, but a comprehensive verification of the model output and experimental data based on clear steady-state accuracy verification requirements (such as specifying that the exhaust temperature error must be less than ±5%). If errors are found to exceed the standard at certain operating points, the process returns to step 122 for further iterative optimization of parameters until the accuracy under all steady-state conditions meets the requirements. The resulting "calibrated steady-state calibration model" is a rigorously verified and reliable benchmark model. All subsequent model extensions and parameter adjustments for transient conditions in steps 13-18 will be carried out using this high-precision steady-state model as the "benchmark," thereby ensuring that the transient model will not deviate from the basic physical relationships.

[0074] By employing a three-step approach of "structural modeling → parameter calibration → accuracy verification," a high-precision engine physical model under steady-state conditions was systematically constructed and verified. The successful implementation of this step provided a crucial physical foundation and starting point for the entire invention, ensuring that all subsequent refined modeling and innovative corrections for transient conditions had a reliable premise and correct reference, avoiding the risk of complex extensions based on errors. Without the solid foundation laid in this step, subsequent improvements in transient accuracy would be impossible.

[0075] like Figure 1 As shown in Figure 13, based on the steady-state calibration model, a transient operating condition decomposition preset is performed, dividing the operating conditions into idling, reverse dragging, and normal ignition conditions, resulting in the operating condition classification results, including:

[0076] 131. Based on the output characteristics of the steady-state calibration model, perform transient road spectrum analysis to obtain the operating condition variation law;

[0077] 132. Based on the changing patterns of operating conditions and using the cyclic fuel injection quantity as the logical judgment boundary, the operating conditions are divided into categories. The idling condition is defined as the fuel injection quantity being lower than the threshold, the reverse drag condition is defined as the fuel injection quantity being zero, and the normal ignition condition is defined as the fuel injection quantity being normal, thus obtaining the operating condition classification results.

[0078] In this embodiment of the invention, the "transient condition decomposition" implemented in step 13 is a key bridge connecting the high-precision steady-state model and the high-precision transient model, and is a strategic innovation; its effect is to fundamentally change the approach to dealing with complex transient problems.

[0079] Step 131 achieves a "divide and conquer" approach to complex transient problems, providing a strategic foundation for accurate modeling. It deconstructs the continuous, variable, and nonlinear transient process into several typical states with clear physical mechanisms, thus simplifying the complex; it helps understand the dynamic behavior of the transient process; and by analyzing the "transient path spectrum," the system grasps the patterns of drastic changes in engine speed and load during real-world operation (such as actual driving cycles), clarifying the operating conditions requiring detailed modeling. Step 132 establishes clear and executable classification rules; it abandons the traditional approach of treating the transient process as a whole for "fuzzy" processing, creatively choosing the cyclic fuel injection quantity—a parameter that is easy to monitor and directly reflects the engine's operating state. The core parameters serve as the boundaries for logical judgments; zero fuel injection quantity → reverse towing condition: the engine is towed by the vehicle, does not burn power, and the exhaust energy source and temperature field characteristics are completely different from those during ignition; fuel injection quantity below the threshold → idling condition: combustion is unstable, exhaust flow is small, heat accumulation effect is significant, and heat exchange conditions are poor (almost adiabatic); normal fuel injection quantity → normal ignition condition: combustion is complete, exhaust energy is high, and flow and heat exchange characteristics conform to the conventional model; this decomposition allows subsequent steps (14, 15) to define the most critical physical processes (such as heat exchange boundary conditions) for each condition, thereby achieving precise modeling of "one policy for one type"; this is the fundamental premise for achieving transient accuracy that surpasses traditional methods;

[0080] By identifying and focusing on the differences in the dominant physical mechanisms under different operating conditions, this model accurately captures the core physical factors affecting exhaust temperature at different transient stages. Under reverse drag conditions, the core physical mechanism is the ambient cooling effect; because there is no combustion, the temperature drop is mainly determined by the heat exchange between cold air flowing through the hot exhaust pipe and the environment. Under idling conditions, the core physical mechanism is the near-adiabatic heat retention of the system; due to the extremely small exhaust flow rate, heat is difficult to dissipate, resulting in the exhaust system wall and gas temperatures remaining high. Under normal ignition conditions, the core physical mechanism is the balance between combustion heat generation and fluid convection heat transfer. By classifying operating conditions, this model clearly indicates under what circumstances a particular physical mechanism should be activated or emphasized. This allows for targeted model reconstruction, avoiding the problem of accuracy loss that inevitably results from using a single, compromise parameter to describe all physical processes.

[0081] To provide clear "decision signals" for subsequent dynamic model reconstruction, the generated "operating condition classification result" is a real-time, discrete state signal that drives the entire model to intelligently switch between different sub-models. This classification result acts like a "command stick." During simulation, the system determines which operating condition (A, B, or C) it is currently in based on the real-time calculated fuel injection quantity. Based on this determination, the dedicated code module established in steps 14 and 15 will dynamically select the corresponding heat transfer coefficient calculation strategy. For example, when the signal indicates "idling," the model will call the preset "adiabatic" or "low heat transfer coefficient" module. This enables the originally static engine model to dynamically respond to changes in operating conditions, evolving from a "single-mode" model into a "multi-mode" intelligent model, which is the key to its ability to accurately track transient exhaust temperature.

[0082] By using intelligent operating condition decomposition based on fuel injection quantity, a clear and effective solution framework is provided for dealing with complex transient exhaust temperature problems. It transforms a continuous nonlinear problem into a combination of several quasi-steady-state linear problems, achieving a "dimensionality reduction attack" on the problem. This provides an indispensable logical foundation and decision-making basis for subsequent accurate model reconstruction for different physical mechanisms. This step is the strategic turning point for this invention from "steady-state accuracy" to "transient accuracy".

[0083] like Figure 1 As shown in Figure 14, based on the operating condition classification results, the exhaust temperature heat transfer model interface is modified and a signal stream is added, and a related code module is used to control the heat transfer process between gas and solid, and between solid and the environment, to obtain a preliminary heat transfer control model, including:

[0084] 141. Based on the above operating condition classification results, modify the exhaust temperature heat transfer model interface in the AVLCruiseM software to obtain the updated interface structure.

[0085] 142. Based on the requirements of the updated interface, perform signal flow addition processing, including the input of the electronic control data oil quantity signal and the output of the heat transfer coefficient signal, to obtain the signal flow network;

[0086] 143. Based on the working condition classification results and signal flow network, program the code module to control the heat transfer coefficient setting between gas and solid, and obtain the gas-solid heat transfer control module.

[0087] 144. Based on the operating condition classification results and signal flow network, the code module is expanded to control the setting of the heat transfer coefficient between the solid and the environment, including setting the adiabatic conditions under idling conditions, to obtain a preliminary heat transfer control model.

[0088] In this embodiment of the invention, step 14 is the core technological innovation for achieving high-precision simulation of transient exhaust temperature. It transforms the strategic judgment of "operating condition classification" generated in step 13 into executable, dynamic physical control logic within the model, thereby completely changing the poor performance of traditional models under transient conditions. Step 141 breaks the black-box limitation of commercial software, enabling customized reconstruction of the model kernel. By actively modifying the inherent model interface and signal flow of the commercial software (AVLCruiseM), it achieves deep intervention in the standard model calculation process. The standard heat exchange model of commercial software is usually a packaged, "black-box" component; users can only adjust limited parameters and cannot change its internal logic. Modifying the interface is equivalent to "opening a backdoor" for this black-box component, allowing external signals and logic to intervene in its core calculation process. This is the foundation for achieving all subsequent fine-grained control. This allows the invention to no longer be limited by the standard functions provided by commercial software, and to customize and improve the model's performance in specific scenarios (transient conditions) based on its own deep understanding of the physical process. Step 142 realizes the heat exchange process... Dynamic, condition-specific precise control transforms the heat transfer model from "static, uniform parameters" to "dynamic, condition-specific parameters." A "neural network" for information transmission is constructed, incorporating key information such as "condition judgment results" (from step 13) and "ECU fuel quantity signals" as input signals; simultaneously, the calculated "dynamic heat transfer coefficient" is transmitted as the output signal. This establishes a direct causal chain from condition identification to heat transfer coefficient setting. Sub-steps 143 and 144 (code module programming) act as the brain and decision-making center, executing commands based on the received real-time condition signals. Different algorithms exist: During normal ignition, a standard heat transfer coefficient based on flow rate and temperature may be used for calculation; during idling, a strategy is employed to significantly reduce or even cut off heat exchange between the solid and the environment (by setting adiabatic conditions) to simulate the heat accumulation effect caused by extremely low flow rate; this is the key to accurately simulating the persistently high exhaust temperature during idling; during reverse drag, the effect of ambient cooling may be enhanced; through this dynamic control, the model can more realistically reflect the physical nature of energy exchange within the exhaust system under different transient stages, thereby significantly improving the accuracy of exhaust temperature simulation;

[0089] Accurate energy boundary conditions and precise exhaust temperature simulation for the entire transient model rely heavily on an accurate description of the energy exchange within and between the exhaust system and the external environment. Exhaust temperature is essentially a result of energy balance. The "preliminary heat transfer control model" constructed in step 14 precisely sets the energy exchange rate (i.e., heat transfer coefficient) between the exhaust system (solid components) and the internal combustion gases and the external environment. This provides highly reliable input conditions for calculating the temperature drop of the exhaust gases flowing through the exhaust pipe, catalytic converter, and other components. An accurate heat transfer control model is the cornerstone for ensuring the accuracy of the thermal model prediction for the entire exhaust system. It ensures that the exhaust temperature calculated by the model not only reflects the energy generated by combustion but also realistically includes energy losses along the transfer path, making the final result more reliable.

[0090] Through a series of technical means including "interface modification, signal flow construction, and code programming," the intelligent judgment based on operating conditions is transformed into dynamic and precise control of the heat exchange process of the model. This step realizes a qualitative change in the model from "static" to "dynamic" and from "general" to "specific," providing high-precision boundary conditions for the core physical process (energy exchange). It is the decisive technical link for finally achieving high-precision simulation of transient exhaust temperature. Without this step of in-depth customization, the operating condition division strategy in step 13 would not be able to be implemented and would greatly reduce the innovative value of the entire invention.

[0091] like Figure 1 As shown in Figure 15, based on the operating condition classification results, the exhaust temperature sensor model interface is modified and a signal stream is added, and a related code module is used to control the heat exchange process between the gas and the probe, resulting in a preliminary sensor control model, including:

[0092] 151. Based on the working condition classification results, modify the exhaust temperature sensor model interface to obtain the sensor interface update structure;

[0093] 152. Based on the update requirements of the sensor interface update structure, perform signal flow addition processing, including the transmission of heat exchange coefficient signals, to obtain the sensor signal flow network;

[0094] 153. Based on the working condition classification results and the sensor signal flow network, program the code module to control the convective heat transfer and radiation heat transfer coefficients between the gas and the probe, and obtain the sensor heat exchange control module.

[0095] 154. Based on the sensor signal flow network required for sensor thermal inertia simulation, optimize the code module to adjust the heat transfer factor under different operating conditions and obtain a preliminary sensor control model.

[0096] In this embodiment of the invention, step 15 is another key innovation in achieving high-precision simulation of transient exhaust temperature. It introduces the dynamic response characteristics of "physical sensors" into the model, transforming the simulation output from "ideal theoretical value" into "measurable performance value", thereby greatly improving the comparability and consistency between the model and real bench test data.

[0097] To bridge the "perception gap" between theoretical and measured values ​​and achieve a fair comparison between simulation and experiment, this step simulates the physical characteristics of a real temperature sensor, making the model output closer to the actual measured value rather than an idealized physical quantity. In the real world, the "exhaust temperature" we measure is not the actual instantaneous temperature of the airflow, but rather the "appearance temperature" sensed by the temperature sensor (thermocouple probe). This sensing process involves dynamic characteristics such as thermal inertia and heat transfer delay. Traditional models directly calculate the airflow temperature and use it as the output, which is fundamentally different from the sensor readings obtained experimentally, leading to inexplicable deviations when comparing under transient conditions. Step 15 establishes a "sensor model" to simulate this sensing process. The convective / radiative heat transfer coefficient and thermal inertia programmed and controlled in sub-steps 153 and 154 are precisely to simulate how the probe exchanges heat with the airflow and its own temperature response speed. After this step, the model's final output is no longer the "true gas temperature" of the airflow, but the "appearance temperature" measured by the virtual sensor. (indicatedTemperature) This allows the simulation results to be compared with the bench test data under the same dimension and standard, providing a correct and fair basis for the accuracy verification and parameter optimization in step 16; finely simulate the dynamic response of the sensor to improve transient tracking accuracy, especially by simulating "thermal inertia", enabling the model to reproduce the response lag and filtering effect of the real sensor when the operating conditions change rapidly; sub-step 154 ​​The real sensor probe has mass and heat capacity, and its temperature change always lags behind the rapid change of airflow temperature; this lag is particularly obvious during rapid acceleration or deceleration; the present invention simulates this effect by dynamically adjusting the heat transfer factor under different operating conditions through code modules; for example, when the airflow temperature rises sharply, the characteristic of the probe temperature slowly following is simulated; when idling, the characteristic of its temperature slowly decreasing is simulated; this makes the exhaust temperature curve obtained by simulation no longer an idealized, sharp waveform with sharp edges, but a smoother curve that is closer to the measured data, which can accurately capture the phase and amplitude details of temperature changes during transient processes, and significantly improve the transient tracking capability of the model;

[0098] Together with the heat transfer model, they form a complete thermal dynamics simulation of the exhaust system. The sensor model in step 15 and the heat transfer model in step 14 each have their own functions, yet they work closely together to accurately depict the entire process from energy generation to sensing. Step 14 (heat transfer model) is responsible for simulating the energy flow and loss inside the exhaust system, answering the question "What is the theoretical temperature of the airflow reaching the sensor location?" It focuses on system-level thermodynamics. Step 15 (sensor model) is responsible for simulating how the sensor senses the airflow temperature, answering the question "What temperature will the sensor actually read?" It focuses on the thermophysics of the local measurement point. Synergistic effect: The combination of the two forms a complete, closed-loop simulation chain from "combustion heat generation" → "pipeline heat transfer" → "sensor sensing". This chain completely reproduces the entire process of exhaust temperature signal generation, transmission, and measurement in the real world, ensuring the high fidelity of the final simulation results.

[0099] like Figure 1 As shown in Figure 16, based on the preliminary heat transfer control model and the preliminary sensor control model, the accuracy of the simulated exhaust temperature is judged. If the accuracy does not meet the preset requirements, adjustments are made based on the difference between the transient test data and the simulation results to obtain an optimized transient model, including:

[0100] 161. Based on the integrated output of the preliminary heat exchange control model and the preliminary sensor control model, calculate the exhaust temperature to obtain the initial simulation results;

[0101] 162. Based on the preset accuracy threshold and the initial simulation results, compare and analyze the simulation results with the transient test data to obtain the accuracy deviation data;

[0102] 163. Based on the accuracy deviation data, iteratively adjust the calibration parameters in the code module, including optimizing the heat transfer coefficient and heat exchange coefficient, to obtain the adjusted control module;

[0103] 164. Perform multiple rounds of iterative verification on the adjusted control module and re-evaluate the model accuracy until the simulated exhaust temperature meets the preset requirements, thus obtaining the optimized transient model.

[0104] In this embodiment of the invention, step 16 is the core optimization closed loop that ensures the transient exhaust temperature model meets the engineering practical accuracy requirements, transforming a theoretically feasible but unknown "preliminary model" into a "high-precision model" that has been rigorously verified and whose results are reliable.

[0105] A closed-loop automatic calibration process of "simulation-verification-optimization" is formed to establish model confidence. A closed-loop verification and iterative optimization mechanism based on measured data is introduced, improving model accuracy from "rough estimation" to "quantitative achievement." Sub-steps 161 and 162 (calculation and comparison) establish an accuracy benchmark. They systematically and quantitatively compare the simulation results of the initial model with the gold standard—"transient test data"—generating objective "accuracy deviation data." This step visualizes and quantifies the model's performance shortcomings, clearly indicating the optimization direction and goals. Sub-step 163 (iterative adjustment) is the core optimization engine. It is not blind adjustment but rather targeted optimization of key calibration parameters (such as heat transfer coefficient and heat exchange coefficient) in the code modules of steps 14 and 15 based on the deviation data. This is a data-driven scientific parameter tuning process based on engineering insights. This closed-loop process closely combines engineers' experience (reflected in adjustment strategies) with objective data (accuracy deviation), systematically driving the model accuracy towards a preset threshold, ultimately ensuring the model's credibility.

[0106] To achieve precise positioning of model parameters from "theoretical values" to "optimal solutions," data feedback is used to find the unique parameter combination that most accurately reflects the actual transient thermodynamic behavior of a specific engine. The initial heat transfer coefficient and heat exchange coefficient set in steps 14 and 15 are usually theoretical estimates based on physical formulas or experience. Due to the complexity of engine systems and sensor responses, these theoretical values ​​cannot perfectly match the real situation. The role of step 16 is to use the "true value" of transient test data to "refine" these parameters. For example, by comparing and finding that the model has a slow exhaust temperature response during rapid acceleration, the transient heat transfer coefficient between the gas and the pipe wall can be improved in a targeted manner. After multiple iterations, these key parameters inside the model are no longer just physical symbols, but are given "life" that can accurately reproduce the dynamic characteristics of a specific real engine, thus making the virtual model a highly faithful digital twin of the real engine.

[0107] To ensure the model possesses the ability and robustness to solve practical engineering problems, the following results are achieved: Through multiple verifications covering various transient conditions, the optimized model is ensured to perform stably and reliably under different scenarios. Sub-step 164 (multi-round iterative verification) is crucial; it requires the optimized model to be re-verified in a complete transient test cycle including multiple conditions such as idling, reverse drag, and normal ignition, rather than just through testing a single condition. This multi-condition, multi-round verification effectively avoids the model "overfitting" to a specific data segment, thereby ensuring its predictive robustness and generalization ability throughout the entire working range. The "optimized transient model" refined through this step is no longer a fragile "theoretical toy," but a robust tool that can reliably predict engine exhaust temperature under various complex transient conditions, fully capable of handling practical engineering tasks such as control strategy development and thermal management.

[0108] like Figure 1 As shown in Figure 17, based on the optimized transient model, the code modules, interfaces, and signal flows are fixed to obtain the calibrated transient engine model, including:

[0109] 171. Based on the stability verification of the optimized transient model, the parameters of the code module are solidified to obtain a fixed code module;

[0110] 172. Based on the fixed code module and interface compatibility requirements, perform interface and signal flow locking to obtain a fixed interface structure;

[0111] 173. Based on the fixed interface structure and model portability requirements, save and encapsulate the overall structure to obtain the calibrated transient engine model.

[0112] In this embodiment of the invention, step 17 is a crucial step in transforming the high-precision transient engine model from "experimental results" to "industrial products." It standardizes, solidifies, and encapsulates the rigorously optimized model, ensuring it can be reused and integrated as a stable and reliable tool, thereby truly unlocking its engineering application value. This ensures the stability of model parameters and the reproducibility of results. By solidifying the optimized parameters, the risk of simulation result distortion due to unexpected parameter changes during model use is eliminated, guaranteeing the consistency and credibility of simulation conclusions. Sub-step 171 (parameter solidification) is the final confirmation of the iterative optimization results from step 16. Parameters such as the heat transfer coefficient and heat exchange coefficient obtained through multiple rounds of optimization directly reflect the model's high precision. This step locks these parameters in the code module to prevent any unintentional or arbitrary modifications. This ensures that today, a month later, or a year later, using the model to simulate the same operating condition will yield completely consistent results. This reproducibility is a fundamental prerequisite for the model to serve as an authoritative verification tool and is the cornerstone of engineering credibility.

[0113] To ensure the ease of model integration and the reliability of interfaces, the model is made a standard, well-defined "component" by locking interfaces and signal flows, making it easy for large systems (such as vehicle virtual development platforms) to call and integrate it. Sub-step 172 (interface and signal flow locking) is the final definition of the rules for interaction between the model and the external environment. It clarifies what input signals the model needs (such as fuel injection quantity and engine speed) and what output signals it will produce (such as exhaust temperature), and fixes the paths and formats for these data exchanges. This allows the transient engine model to be called stably and error-free by other developers or systems, just like a standard software library, without needing to worry about its complex internal logic, only needing to follow the defined interface protocol. This greatly reduces integration complexity and avoids connection errors caused by interface mismatch.

[0114] The process of encapsulating, saving, and efficiently reusing model results involves packaging the complete model and its configuration into an independent, deliverable digital asset, greatly improving development efficiency. Sub-step 173 (Saving and Encapsulation) is the "archiving" and "productizing" of the entire modeling, calibration, and optimization results. The generated model package contains fixed parameters, locked interfaces, and all dependencies. It is easy to port: the encapsulated model can be easily deployed from the development environment to the testing environment or different simulation platforms. It facilitates collaboration: it can be distributed as a standard component to different teams or projects, promoting knowledge reuse and avoiding redundant development. It forms an asset: this high-precision model itself becomes an important digital asset for the enterprise, which can be continuously used for subsequent controller design, testing, verification, and other work, and its value can be continuously accumulated.

[0115] like Figure 2 As shown, a system 20 based on AVLCruiseM for calibrating the exhaust temperature of a virtual engine model under transient operating conditions includes:

[0116] Module 21 is used to acquire engine physical structure parameters and performance test data, perform data preprocessing, and obtain a basic dataset.

[0117] Processing module 22 is used to construct a basic engine model and calibrate a steady-state model based on the basic dataset to obtain a calibrated steady-state model; according to the steady-state calibration model, it performs transient operating condition decomposition preset, dividing the operating conditions into idling, reverse dragging, and normal ignition operating conditions to obtain operating condition classification results; according to the operating condition classification results, it modifies the exhaust temperature heat transfer model interface and adds signal streams, and associates code modules to control the heat transfer process between gas and solid, and between solid and the environment, to obtain a preliminary heat transfer control model; according to the operating condition classification results, it modifies the exhaust temperature sensor model interface and adds signal streams, and associates code modules. A preliminary sensor control model is obtained by controlling the heat exchange process between the gas and the probe. Based on the preliminary heat exchange control model and the preliminary sensor control model, the accuracy of the simulated exhaust temperature is judged. If the accuracy does not meet the preset requirements, adjustments are made based on the difference between the transient test data and the simulation results to obtain an optimized transient model. Based on the optimized transient model, the code modules, interfaces, and signal flows are fixed to obtain a calibrated transient engine model. Based on the calibrated transient engine model, it is integrated into a virtual development platform to obtain a virtual engine system that can be accurately controlled in an online environment.

[0118] Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention.

[0119] like Figure 3 As shown, the electronic device may include a processor 610, a communications interface 620, a memory 630, and a communication bus 640. The processor 610, communications interface 620, and memory 630 communicate with each other via the communication bus 640. The processor 610 can call logical instructions from the memory 630 to execute a method based on AVLCruiseM for calibrating the transient exhaust temperature of a virtual engine model.

[0120] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0121] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the methods provided above for calibrating the exhaust temperature of a virtual engine model under transient operating conditions based on AVLCruiseM.

[0122] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the methods provided above for calibrating the exhaust temperature of a virtual engine model under transient operating conditions based on AVLCruiseM.

[0123] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0124] The foregoing description of specific exemplary embodiments of the invention is for illustrative and explanatory purposes. These descriptions are not intended to limit the invention to the precise forms disclosed, and it will be apparent that many changes and variations can be made in accordance with the foregoing teachings. The exemplary embodiments were chosen and described in order to explain the specific principles of the invention and its practical application, thereby enabling those skilled in the art to implement and utilize various different exemplary embodiments of the invention, as well as various different choices and variations. The scope of the invention is intended to be defined by the claims and their equivalents.

Claims

1. A method for calibrating the exhaust temperature of a virtual engine model under transient operating conditions based on AVLCruiseM, characterized in that, include: Obtain engine physical structure parameters and performance test data, perform data preprocessing, and obtain the basic dataset; Based on the aforementioned basic dataset, a basic engine model is constructed and a steady-state model is calibrated to obtain the calibrated steady-state model. Based on the steady-state calibration model, a transient operating condition decomposition preset is performed, and the operating conditions are divided into idling, reverse dragging and normal ignition operating conditions to obtain the operating condition classification results. Based on the operating condition classification results, the exhaust temperature heat transfer model interface is modified and signal streams are added, and code modules are associated to control the heat transfer process between gas and solid, and between solid and environment, to obtain a preliminary heat transfer control model. Based on the operating condition classification results, the exhaust temperature sensor model interface was modified and a signal stream was added. The associated code module was used to control the heat exchange process between the gas and the probe, thus obtaining a preliminary sensor control model. Based on the preliminary heat exchange control model and the preliminary sensor control model, the accuracy of the simulated exhaust temperature is judged. If the accuracy does not meet the preset requirements, adjustments are made based on the difference between the transient test data and the simulation results to obtain the optimized transient model. Based on the optimized transient model, the code modules, interfaces, and signal flows are fixed to obtain the calibrated transient engine model. Based on the calibrated transient engine model, it is integrated into the virtual development platform to obtain a virtual engine system that can be accurately controlled in an online environment.

2. The method for calibrating the exhaust temperature of a virtual engine model under transient operating conditions based on AVLCruiseM according to claim 1, characterized in that, Based on the aforementioned basic dataset, a basic engine model is constructed and a steady-state model is calibrated to obtain the calibrated steady-state model, including: Based on the engine structure data in the aforementioned basic dataset, a basic engine model is constructed using the MOBEOCylinder class model. Based on the experimental data in the aforementioned basic dataset, the engine steady-state operating condition is calibrated, including the optimization of combustion efficiency and heat transfer parameters, to obtain a preliminary steady-state model. Based on the basic engine model and the preliminary steady-state model, and according to the steady-state accuracy verification requirements, the model output and experimental data are compared and analyzed to obtain the calibrated steady-state calibration model.

3. The method for calibrating the exhaust temperature of a virtual engine model under transient operating conditions based on AVLCruiseM according to claim 2, characterized in that, Based on the steady-state calibration model, a transient operating condition decomposition is preset, dividing the operating conditions into idling, reverse driving, and normal ignition conditions, resulting in the following operating condition classification results: Based on the output characteristics of the steady-state calibration model, transient road spectrum analysis is performed to obtain the operating condition variation law; Based on the changing patterns of operating conditions and using the cyclic fuel injection quantity as the logical judgment boundary, the operating conditions are classified. The idling condition is defined as the fuel injection quantity being lower than the threshold, the reverse dragging condition is defined as the fuel injection quantity being zero, and the normal ignition condition is defined as the fuel injection quantity being normal, thus obtaining the operating condition classification results.

4. The method for calibrating the exhaust temperature of a virtual engine model under transient operating conditions based on AVLCruiseM according to claim 3, characterized in that, Based on the operating condition classification results, the exhaust temperature heat transfer model interface is modified and signal streams are added. A related code module is then used to control the heat transfer process between gas and solid, and between solid and the environment, resulting in a preliminary heat transfer control model, including: Based on the operating condition classification results, the exhaust temperature heat transfer model interface in the AVLCruiseM software was modified to obtain the updated interface structure. Based on the requirements of the updated interface, signal flow addition processing is performed, including the input of electronic control data oil quantity signal and the output of heat transfer coefficient signal, to obtain the signal flow network; Based on the operating condition classification results and signal flow network, code modules are programmed to control the heat transfer coefficient setting between gas and solid, thus obtaining a gas-solid heat transfer control module. Based on the operating condition classification results and signal flow network, the code module is expanded to control the setting of the heat transfer coefficient between the solid and the environment, including setting the adiabatic conditions under idling conditions, to obtain a preliminary heat transfer control model.

5. The method for calibrating the exhaust temperature of a virtual engine model under transient operating conditions based on AVLCruiseM according to claim 4, characterized in that, Based on the operating condition classification results, the exhaust temperature sensor model interface is modified and a signal stream is added. A related code module is then used to control the heat exchange process between the gas and the probe, resulting in a preliminary sensor control model, including: Based on the operating condition classification results, the exhaust temperature sensor model interface is modified to obtain the sensor interface update structure; Based on the update requirements of the sensor interface update structure, signal flow addition processing is performed, including the transmission of heat exchange coefficient signals, to obtain the sensor signal flow network; Based on the working condition classification results and the sensor signal flow network, code modules are programmed to control the convective heat transfer and radiative heat transfer coefficients between the gas and the probe, thus obtaining the sensor heat exchange control module. Based on the sensor signal flow network required for sensor thermal inertia simulation, the code module is optimized to adjust the heat transfer factor under different operating conditions, thus obtaining a preliminary sensor control model.

6. The method for calibrating the exhaust temperature of a virtual engine model under transient operating conditions based on AVLCruiseM according to claim 5, characterized in that, Based on the preliminary heat transfer control model and the preliminary sensor control model, the accuracy of the simulated exhaust temperature is judged. If the accuracy does not meet the preset requirements, adjustments are made based on the difference between the transient test data and the simulation results to obtain an optimized transient model, including: Based on the integrated output of the preliminary heat exchange control model and the preliminary sensor control model, the exhaust temperature is calculated to obtain the initial simulation results; Based on the preset accuracy threshold and the initial simulation results, the simulation results are compared and analyzed with the transient test data to obtain the accuracy deviation data; Based on the accuracy deviation data, the calibration parameters in the code module are iteratively adjusted, including the optimization of heat transfer coefficient and heat exchange coefficient, to obtain the adjusted control module; The adjusted control module was iteratively verified multiple times, and the model accuracy was re-evaluated until the simulated exhaust temperature met the preset requirements, thus obtaining the optimized transient model.

7. The method for calibrating the exhaust temperature of a virtual engine model under transient operating conditions based on AVLCruiseM according to claim 6, characterized in that, Based on the optimized transient model, the code modules, interfaces, and signal flows are fixed to obtain the calibrated transient engine model, including: Based on the stability verification of the optimized transient model, the parameters of the code module are solidified to obtain a fixed code module; Based on the fixed code module and interface compatibility requirements, interface and signal flow locking is performed to obtain a fixed interface structure; Based on the fixed interface structure and model portability requirements, the overall structure is saved and encapsulated to obtain the calibrated transient engine model.

8. A system based on AVLCruiseM for calibrating the exhaust temperature of a virtual engine model under transient operating conditions, characterized in that, include: The acquisition module is used to acquire engine physical structure parameters and performance test data, perform data preprocessing, and obtain a basic dataset. The processing module is used to construct a basic engine model and calibrate a steady-state model based on the aforementioned basic dataset, obtaining a calibrated steady-state model. Based on the steady-state calibration model, it performs transient condition decomposition pre-setting, classifying the conditions into idling, reverse drag, and normal ignition conditions, obtaining condition classification results. Based on the condition classification results, it modifies the exhaust temperature heat transfer model interface and adds signal streams, and associates code modules to control the heat transfer process between gas and solid, and between solid and the environment, obtaining a preliminary heat transfer control model. Based on the condition classification results, it modifies the exhaust temperature sensor model interface and adds signal streams, and associates code modules to... The heat exchange process between the controlled gas and the probe is used to obtain a preliminary sensor control model. Based on the preliminary heat exchange control model and the preliminary sensor control model, the accuracy of the simulated exhaust temperature is judged. If the accuracy does not meet the preset requirements, adjustments are made based on the difference between the transient test data and the simulation results to obtain an optimized transient model. Based on the optimized transient model, the code modules, interfaces, and signal flows are fixed to obtain a calibrated transient engine model. Based on the calibrated transient engine model, it is integrated into a virtual development platform to obtain a virtual engine system that can be accurately controlled in an online environment.

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 executes the program, it implements the method for calibrating the exhaust temperature of a virtual engine model under transient operating conditions based on AVLCruiseM as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for calibrating the exhaust temperature of a virtual engine model under transient operating conditions based on AVLCruiseM as described in any one of claims 1 to 7.

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