A simulation development and verification method for power assemblies suitable for different emission standards

Abstract

This invention discloses a simulation development and verification method and system for powertrains applicable to different emission standards. The method includes: determining core benchmark parameters of the powertrain, setting test variables covering the environmental characteristics of the target area, conducting system tests under different emission standard test conditions, collecting operating condition, emission, energy consumption, and system operation data, and establishing a raw database after standardization processing. Based on the data, a working condition-emission-energy consumption correlation model and a multi-factor influence law model are constructed to analyze the "engine-emission aftertreatment" control logic. A comprehensive database is built, and an electronic control adaptive optimization mechanism is designed to adapt to the scenario. Simulation verification is performed according to the target standard; if the standard is not met, parameters are optimized; after the standard is met, real vehicle verification is performed to form the final solution. Each step is progressive and closed-loop, ensuring that the powertrain adapts to different emission requirements, the solution is compliant and reliable, and energy economy and operational stability are considered, thereby improving development efficiency.

Description

Technical Field

[0001] This invention belongs to the field of automotive powertrain control, and more specifically, relates to a simulation development and verification method and system for powertrains applicable to different emission standards. Background Technology

[0002] With increasing global environmental awareness, countries and regions have successively introduced stricter vehicle emission standards. Significant differences exist in emission limits and testing conditions across different regions, placing higher demands on the adaptability of powertrains. Currently, the automotive industry faces the need for multi-regional market deployment, requiring the same powertrain to adapt to the emission regulations of different countries or regions. This practical requirement presents a significant challenge to the development and validation of powertrains.

[0003] Traditional powertrain development often focuses on a single emission standard or specific scenario, lacking a comprehensive consideration of different environmental characteristics, driving behaviors, and testing conditions. In real-world applications, changes in environmental factors such as altitude and ambient temperature directly affect engine combustion efficiency and after-treatment system performance, leading to fluctuations in pollutant emissions. Some models are prone to exceeding emission standards under non-design conditions. Furthermore, the traditional development model relies heavily on extensive real-vehicle testing, which not only incurs huge costs but also suffers from long development cycles and low efficiency, making it difficult to quickly respond to the market's urgent need for multi-emission standard compatibility.

[0004] In existing technologies, powertrain control strategies are mostly based on fixed parameter settings, which cannot be dynamically adjusted according to actual operating scenarios, making it difficult to balance emission control and energy economy. When facing the switching of different emission standards, a large amount of recalibration work is often required, further increasing the development difficulty and cost. In addition, traditional modeling methods rely heavily on instantaneous parameters, failing to fully capture the impact of dynamic changes in operating conditions and cumulative effects on emissions and energy consumption. The model prediction accuracy is insufficient, making it difficult to provide reliable support for control strategy optimization.

[0005] With increasingly stringent environmental regulations and intensifying market competition, developing a powertrain validation method that can adapt to different emission standards while balancing environmental adaptability and performance stability has become a pressing issue for the industry. Solving this problem can not only reduce development costs and shorten development cycles for multi-regional adaptation, but also improve the emission control accuracy and energy economy of powertrains. This will help companies meet regulatory requirements in different global markets, enhance product market competitiveness, and has significant practical implications and industry value. Summary of the Invention

[0006] This invention aims to solve the powertrain adaptation challenges posed by differences in emission standards across multiple regions and complex environmental conditions, overcoming the limitations of traditional development models such as high cost, long development cycles, and poor adaptability. Through data integration, model building, and adaptive optimization, it achieves accurate matching of control strategies with different emission regulations and environmental scenarios, ensuring emission compliance while balancing energy consumption and performance, reducing development costs, and enhancing product market competitiveness.

[0007] To address the aforementioned deficiencies or improvement needs of existing technologies, as a first aspect of this invention, the present invention provides a simulation development and verification method for powertrains applicable to different emission standards, comprising: S1. Determine the core benchmark parameters of the powertrain system and set test variables to cover the environmental characteristics of the target application area; conduct powertrain system tests for test conditions corresponding to different emission standards, collect operating condition parameters, pollutant emission data, energy consumption data and system operating status data, standardize the collected data, and establish an original database; S2. Based on the standardized raw data, construct a correlation model between operating parameters and emission and energy consumption data; combine test variables to establish a model of the influence of multiple factors, including environment and driving behavior, on emission temperature, catalyst performance, and emission energy consumption performance; through the correlation model and the influence model, analyze the control logic relationship of the "engine-emission aftertreatment" system under different application scenarios. S3. Establish a comprehensive database of "environmental conditions - power parameters - catalyst parameters - emission energy consumption" and integrate raw data with model analysis results; based on this database, design an adaptive optimization mechanism for the electronic control system, enabling the electronic control system to dynamically adjust the basic model parameters according to the characteristics of the actual operating environment, thereby optimizing at least one of the engine air-fuel ratio, exhaust gas recirculation rate, regeneration threshold, and emission system heating strategy, so as to achieve the matching of the "engine-aftertreatment" control strategy with the application scenario and test conditions; S4. Based on the emission standards and regulations of the target area and the corresponding test conditions, construct a powertrain system simulation model based on the optimized control strategy, and conduct compliance and performance simulation tests. If the simulation results do not meet the target regulatory requirements, return to the control strategy optimization step to adjust the parameters until the simulation meets the standards. Verify the control strategy after compliance with the standards on a real vehicle, check the compliance of emission and energy consumption indicators, form the final matching scheme, and complete the development verification.

[0008] Furthermore, the test variables in S1 include at least one of altitude, ambient temperature, and driving mode.

[0009] Furthermore, the association model in S2 is specifically as follows: Let the instantaneous velocity under the test condition be... Instantaneous acceleration is The specific power of motor vehicles is ,Depend on , and overall vehicle quality It is derived that ;in It is the acceleration due to gravity. The rolling resistance coefficient, Road slope; by , , As the core input variable, the model output is the instantaneous pollutant emissions. With instantaneous energy consumption Their mathematical relationship satisfies: in, For time variables, It characterizes the cumulative dynamic features of operating parameters, reflecting the cumulative changes in vehicle power demand per unit time. It represents the cumulative effect of the vehicle's power output, which corresponds to the lagging impact of the continuous accumulation of power demand during actual driving on emissions and energy consumption. , This is a dynamic mapping function built based on standardized raw data.

[0010] Furthermore, the influence law model in S2 is specifically as follows: Let the altitude be The ambient temperature is Driving mode is The dynamic characteristics of the operating parameters are as follows: ,by , , , As the core input variable; the model output is the emission temperature. Catalyst performance indicators Comprehensive indicators of emission energy consumption performance Their mathematical relationship satisfies: in, For time variables, For integration time variable, It characterizes the cumulative effect of the vehicle's power demand and reflects the continuous effect of engine load under different operating conditions. The cumulative effect of emission temperature is characterized, which aligns with the dynamic change in catalyst activity over time. The time-cumulative effect characterizing catalyst performance reflects its sustained impact on emissions and energy consumption; , , It is a mapping function constructed based on heat and mass transfer theory, chemical reaction kinetics principles, and standardized raw data.

[0011] Furthermore, the parsing process of the control logic association in S2 is as follows: Assume the core control parameters include the engine air-fuel ratio. Exhaust gas recirculation rate Regeneration threshold Characterization parameters of emission system heating strategy ; Based on instantaneous pollutant emissions Instantaneous energy consumption is Emission temperature affecting the output of the law model Catalyst performance indicators Comprehensive indicators of emission energy consumption performance ,altitude Ambient temperature Driving mode The time variable is For the input variables, construct a dynamic relationship between the control parameters and the input variables, satisfying: in, For the specific power of motor vehicles, The optimal operating temperature for the catalyst, Characterizing the synergistic cumulative effect between emission temperature and catalyst performance, reflecting the core correlation factor for air-fuel ratio regulation, This characterizes the cumulative impact of vehicle power demand on the exhaust gas recirculation rate. Characterizing the coupled cumulative effect of catalyst performance and emission temperature provides a quantitative basis for determining the regeneration threshold. Characterize the cumulative deviation between the emission temperature and the optimal operating temperature of the catalyst to clarify the direction of the control of the emission system heating strategy; , , , It is a mapping function constructed based on the output data of the correlation model and the influence law model, combined with engine combustion theory and after-treatment chemical reaction mechanism.

[0012] Furthermore, the adaptive optimization mechanism in S3 is specifically as follows: This mechanism collects real-time environmental and operating condition data during actual operation through the electronic control system, combining this data with dynamic characteristics of operating parameters and real-time monitoring data of emission temperature and catalyst performance. Quantitative values ​​of the deviation between environmental operating conditions and standard operating conditions. ,in For altitude, For ambient temperature, Driving mode For speed, For acceleration, For emission temperature, For catalyst performance indicators, This is the deviation quantization function; Based on deviation quantization value ,pass Dynamically adjust the base model parameters ,in For time variables, For integration time variable, Characterizing the cumulative effect of bias, This is the function for adjusting the basic model parameters; combined with the adjusted basic model parameters, the engine air-fuel ratio is optimized using the formulas below. Exhaust gas recirculation rate Regeneration threshold Characterization parameters of emission system heating strategy : in, Instantaneous pollutant emissions, For instantaneous energy consumption, For the specific power of motor vehicles, The optimal operating temperature for the catalyst, , , , This is a function for optimizing control parameters.

[0013] Furthermore, the process in S3 that optimizes at least one of the engine air-fuel ratio, exhaust gas recirculation rate, regeneration threshold, and emission system heating strategy specifically includes: Its core function is to adapt the system to different application scenarios, test conditions, and target emission standards by dynamically adjusting the core control parameters of the "engine-aftertreatment" system. Specifically, this includes: Engine air-fuel ratio optimization: Based on the characteristics of actual environmental conditions and the performance of catalysts, the air-fuel mixing ratio is dynamically adjusted to ensure the rationality of engine combustion. While improving energy utilization efficiency, it reduces the amount of pollutants generated and provides favorable conditions for the conversion of pollutants in the after-treatment system. Optimization of exhaust gas recirculation rate: By combining driving mode, vehicle power demand and pollutant emission control targets, the proportion of exhaust gas recirculation to participate in combustion is adjusted to optimize engine combustion state, achieve a synergistic balance between power performance and pollutant emission reduction, and adapt to the limit requirements of different emission standards. Regeneration threshold optimization: Based on catalyst performance status, emission temperature changes and after-treatment system operation status, dynamically set the after-treatment system regeneration start-up conditions to avoid excessive emissions and unnecessary energy consumption, ensure stable operation of the after-treatment system, and ensure that pollutant conversion efficiency meets target regulatory requirements. Emission system heating strategy optimization: For application scenarios where the catalyst has difficulty reaching the optimal operating temperature quickly, the timing and intensity of heating start-up are adjusted to drive the catalyst temperature to quickly enter the optimal operating range, improve pollutant conversion efficiency, and ensure emission compliance under extreme environments; By optimizing any one of the above control parameters or combining multiple control parameters, the "engine-aftertreatment" control strategy can be dynamically adapted to different environmental conditions, driving modes, and test conditions, ensuring that the powertrain system meets the emission standards of the target export area while taking into account both energy economy and system operation stability.

[0014] Furthermore, the process of conducting compliance and performance simulation testing in S4 is as follows: Based on the optimized engine after-treatment control strategy, a powertrain system simulation model is constructed by integrating core data from the comprehensive database. This model is used to reproduce the test conditions and actual operating environment conditions corresponding to the emission standards of the target area. Using the emission limits of the target area as the compliance judgment benchmark and energy consumption economy as the performance evaluation index, the test condition parameter sequence and the environmental characteristic parameters of the target area are input into the simulation model to simulate the operation of the engine after-treatment system and the entire process of vehicle energy consumption under different operating scenarios. The simulation model outputs pollutant emission data, vehicle energy consumption data, and system operating status data; the pollutant emission data is compared with the limits specified by the emission standards of the target area to complete compliance verification; the energy consumption data is quantitatively analyzed to complete performance evaluation; and a simulation test report containing compliance judgment results, performance evaluation results, and system operating status analysis is generated to provide a basis for subsequent adjustment of control strategy parameters.

[0015] As a second aspect of the present invention, a simulation development and verification system for powertrains applicable to different emission standards is also provided, comprising: The data acquisition and standardization unit is used to determine the core benchmark parameters of the powertrain system and set test variables covering the environmental characteristics of the target application area; for test conditions corresponding to different emission standards, the powertrain system is tested, and operating condition parameters, pollutant emission data, energy consumption data and system operating status data are collected. The collected data is standardized and a raw database is established. The modeling and control logic parsing unit is used to construct a correlation model between operating parameters and emission and energy consumption data based on the standardized raw data; combined with test variables, it establishes a model of the influence of multiple factors, including environment and driving behavior, on emission temperature, catalyst performance, and emission energy consumption performance; and through the correlation model and the influence model, it analyzes the control logic correlation relationship of the "engine-emission aftertreatment" system under different application scenarios. The database construction and strategy optimization unit is used to build a comprehensive database of "environmental conditions - power parameters - catalyst parameters - emission energy consumption", integrating raw data and model analysis results. Based on this database, an adaptive optimization mechanism for the electronic control system is designed, enabling the electronic control system to dynamically adjust the basic model parameters according to the characteristics of the actual operating environment, thereby optimizing at least one of the engine air-fuel ratio, exhaust gas recirculation rate, regeneration threshold, and emission system heating strategy, so as to achieve the matching of the "engine-aftertreatment" control strategy with the application scenario and test conditions. The simulation verification and scheme determination unit is used to construct a powertrain system simulation model based on the optimized control strategy for the emission standards and regulations and corresponding test conditions of the target area, and to conduct compliance and performance simulation tests. If the simulation results do not meet the target regulatory requirements, the unit returns to the control strategy optimization step to adjust the parameters until the simulation meets the standards. The control strategy that meets the standards is then verified on a real vehicle to check the compliance of emission and energy consumption indicators, form the final matching scheme, and complete the development verification.

[0016] As a third aspect of the invention, a computer-readable storage medium is also provided, on which a computer program is stored, which is executed by a processor, according to any one of the methods for simulation development and verification of a powertrain applicable to different emission standards.

[0017] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: 1. The simulation development and verification method for powertrains applicable to different emission standards of this invention is based on multi-scenario data acquisition and standardized processing, covering the environmental characteristics of the target application area and the test conditions corresponding to different emission standards. By systematically collecting operating parameters, pollutant emission data, energy consumption data, and system operating status data, differences in data format and units are eliminated, constructing a complete and standardized raw database, providing reliable data support for subsequent modeling and optimization. This technical feature ensures the comprehensiveness and accuracy of data sources, avoids model distortion caused by data bias, and enables the subsequent development process to conform to actual application scenarios and regulatory requirements, laying a solid data foundation for adaptation to different emission regulations.

[0018] 2. The simulation development and verification method of this invention for powertrains applicable to different emission standards establishes a correlation model between operating parameters and emission and energy consumption data based on standardized raw data. Simultaneously, it builds a multi-factor influence model by combining environmental and driving behavior test variables. Through the synergistic effect of these two models, the control logic correlation of the "engine-emission aftertreatment" system is quantitatively analyzed. This technology overcomes the limitations of traditional modeling that relies solely on instantaneous parameters, fully capturing the impact of dynamic changes in operating conditions and cumulative effects. This allows the model to accurately reflect the emission and energy consumption variation patterns under multi-factor coupling, providing a scientific theoretical basis and quantitative support for control strategy optimization.

[0019] 3. The simulation development and verification method for powertrains applicable to different emission standards in this invention achieves accurate adaptation by building a comprehensive database and designing an adaptive optimization mechanism. It integrates raw data and model analysis results to construct a comprehensive database of "environmental conditions, power parameters, catalyst parameters, and emissions / energy consumption." Based on this database, an adaptive optimization mechanism for the electronic control system is designed to dynamically adjust the basic model parameters and core control parameters. Combined with a closed-loop process of simulation verification and real-vehicle validation, it ensures that the control strategy matches different application scenarios, test conditions, and emission regulations. This technical feature effectively solves the problem of poor adaptability of traditional fixed strategies, ensuring emission compliance while also considering energy economy and system stability, significantly reducing the development cost and cycle time for multi-regional adaptation. Attached Figure Description

[0020] Figure 1 This is a flowchart of the simulation development and verification method for powertrains based on different emission standards according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the system units in an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0022] Example 1 Please refer to Figure 1 Example 1 provides a simulation development and verification method for powertrains applicable to different emission standards, including: S1. Determine the core benchmark parameters of the powertrain system and set test variables to cover the environmental characteristics of the target application area; conduct powertrain system tests for test conditions corresponding to different emission standards, collect operating condition parameters, pollutant emission data, energy consumption data and system operating status data, standardize the collected data, and establish an original database; S2. Based on the standardized raw data, construct a correlation model between operating parameters and emission and energy consumption data; combine test variables to establish a model of the influence of multiple factors, including environment and driving behavior, on emission temperature, catalyst performance, and emission energy consumption performance; through the correlation model and the influence model, analyze the control logic relationship of the "engine-emission aftertreatment" system under different application scenarios. S3. Establish a comprehensive database of "environmental conditions - power parameters - catalyst parameters - emission energy consumption" and integrate raw data with model analysis results; based on this database, design an adaptive optimization mechanism for the electronic control system, enabling the electronic control system to dynamically adjust the basic model parameters according to the characteristics of the actual operating environment, thereby optimizing at least one of the engine air-fuel ratio, exhaust gas recirculation rate, regeneration threshold, and emission system heating strategy, so as to achieve the matching of the "engine-aftertreatment" control strategy with the application scenario and test conditions; S4. Based on the emission standards and regulations of the target area and the corresponding test conditions, construct a powertrain system simulation model based on the optimized control strategy, and conduct compliance and performance simulation tests. If the simulation results do not meet the target regulatory requirements, return to the control strategy optimization step to adjust the parameters until the simulation meets the standards. Verify the control strategy after compliance with the standards on a real vehicle, check the compliance of emission and energy consumption indicators, form the final matching scheme, and complete the development verification.

[0023] This embodiment 1 further elaborates on the above steps.

[0024] (1) Data collection and standardization Given the significant differences in emission standards across different regions globally and the complex and ever-changing environmental conditions, ensuring that powertrain development accurately adapts to target application scenarios and regulatory requirements necessitates first clarifying the fundamental boundaries and data sources for development. The first step is to determine the core baseline parameters of the powertrain system. These parameters serve as the performance baseline for the entire development process, encompassing key information such as basic engine technical parameters and core aftertreatment system configurations, providing a unified reference standard for subsequent testing and optimization. Simultaneously, test variables covering the environmental characteristics of the target application area must be established. Considering the differences in geographical conditions, climate, and user driving habits across different regions, test variables should include at least one of altitude, ambient temperature, and driving mode. By comprehensively covering these key influencing factors, it ensures that subsequent test data reflects the real-world conditions in actual usage scenarios.

[0025] Based on the established core benchmark parameters and test variables, targeted powertrain system testing is conducted. Testing must be conducted around the test conditions corresponding to different emission standards, as the limit requirements and test cycle procedures differ between standards. Only targeted testing can obtain basic data that meets the requirements of various regulations. During the testing process, four key types of data are collected: operating parameters, including dynamic operating information such as speed and acceleration during vehicle operation; pollutant emission data, covering the emission of various controlled pollutants such as hydrocarbons, carbon monoxide, nitrogen oxides, and particulate matter; energy consumption data, recording the energy consumption status during vehicle operation; and system operating status data, reflecting the working status of core components such as the engine and after-treatment system.

[0026] After data collection, all data must be standardized. Different test scenarios and equipment may lead to differences in data format, units, and precision. Direct use of such data can affect the accuracy of subsequent modeling and analysis. Standardization eliminates these differences, ensuring consistent statistical standards and comparability. The processed data will be integrated to establish a complete raw database. This database centrally stores powertrain operation and performance data under different working conditions and environments, providing complete and reliable foundational data support for subsequent correlation model construction, control logic analysis, and strategy optimization. This ensures the entire development process is based on real and valid data.

[0027] (2) Modeling and control logic analysis After the raw data has been standardized, model building and control logic analysis are required to uncover the intrinsic relationship between operating parameters and performance indicators, clarify the comprehensive impact of multiple factors on the system, and thus provide a scientific basis for optimizing control strategies.

[0028] First, based on the standardized raw data, a correlation model is built between operating parameters and emission and energy consumption data. The specific power of the vehicle is... ,Depend on , and overall vehicle quality It is deduced that, ;in It is the acceleration due to gravity. The rolling resistance coefficient, Given the road gradient, the instantaneous speed under the test conditions is: Instantaneous acceleration is ; by , , As the core input variable, the model output is the instantaneous pollutant emissions. It covers hydrocarbons (HC), carbon monoxide (CO), ammonia oxides (NOx), particulate matter (PM), etc., and instantaneous energy consumption. Their mathematical relationship satisfies: in, For time variables, It characterizes the cumulative dynamic features of operating parameters, reflecting the cumulative changes in vehicle power demand per unit time. It represents the cumulative effect of the vehicle's power output, which corresponds to the lagging impact of the continuous accumulation of power demand during actual driving on emissions and energy consumption. , It is a dynamic mapping function constructed based on a standardized vehicle operation test dataset, where Used to output instantaneous pollutant emissions , Used to output instantaneous energy consumption This dataset contains vehicle speeds collected in test cycles using NEDC or WLTC standards under various altitudes, ambient temperatures, and road gradients. acceleration Motor vehicle power ratio Instantaneous pollutant (HC, CO, NOx, PM) emissions and instantaneous energy consumption The correspondence has been established, and preprocessing has been completed through data cleaning, unit unification, extreme value removal, and normalization. In practice, the function can be trained using machine learning algorithms such as random forests, with instantaneous operating condition parameters as input. , , ) and cumulative effect parameter ( , The output is the corresponding instantaneous value; its purpose is to characterize the emission and energy consumption response law under the combined effect of instantaneous changes and cumulative effects of vehicle power demand.

[0029] This method captures the synergistic effect of transient changes and cumulative effects of operating parameters by integrating integral terms, breaking through the limitations of traditional modeling that only relies on instantaneous parameters. It simultaneously represents the real-time response and lag correlation of emission and energy consumption data. Furthermore, the construction process of the functional relationship incorporates the scene characteristics corresponding to the collected test variables such as altitude, ambient temperature, and driving mode, enabling the model to reflect the dynamic evolution of operating conditions and the coupling mechanism of emissions and energy consumption under different application scenarios.

[0030] Based on this, and in conjunction with the established test variables, a model was established to illustrate the influence of multiple factors, including environment and driving behavior, on emission temperature, catalyst performance, and emission energy consumption performance. The specific influence model is as follows: Let altitude be... The ambient temperature is Driving mode is The dynamic characteristics of the operating parameters are as follows: (by speed) acceleration Motor vehicle power ratio (derived from transient changes and cumulative characteristics), with , , , As the core input variable; the model output is the emission temperature. Catalyst performance indicators (Covering catalyst activity, pollutant conversion efficiency), comprehensive indicators of emission energy consumption performance (Covering total emissions of various pollutants, emission intensity per unit of energy consumption, and comprehensive energy consumption of the entire vehicle), the mathematical relationship satisfies: in, For time variables, For integration time variable, It characterizes the cumulative effect of the vehicle's power demand and reflects the continuous effect of engine load under different operating conditions. The cumulative effect of emission temperature is characterized, which aligns with the dynamic change in catalyst activity over time. The time-cumulative effect characterizing catalyst performance reflects its sustained impact on emissions and energy consumption; , , It is a mapping function constructed based on heat and mass transfer theory, chemical reaction kinetics principles, and standardized raw data.

[0031] , , This is a mapping function constructed based on heat and mass transfer theory, chemical reaction kinetics principles, and standardized raw data. Its construction process first requires processing the input environmental parameters (altitude). Ambient temperature Operating parameters (driving mode) Dynamic characteristics of operating conditions Motor vehicle power ratio cumulative term ) and status parameters (emission temperature) Catalyst performance indicators The data (including the corresponding cumulative terms) undergoes data cleaning and dimensionless preprocessing to eliminate dimensional differences and outlier interference. Secondly, based on the energy conservation equation in heat and mass transfer theory and the Arrhenius equation in chemical reaction kinetics, the theoretical correlation weights between input parameters and output quantities are determined. Finally, machine learning or deep learning algorithms, such as BP neural networks, are used to train the preprocessed data to construct a dynamic mapping relationship. The error range between the output emission temperature, catalyst performance indicators, and comprehensive emission energy consumption performance indicators and the measured values ​​is determined. Ultimately, the goal is to characterize the dynamic response of emission temperature, catalyst performance, and emission energy consumption under the coupled effects of multiple factors.

[0032] By integrating the multi-physics coupling mechanism with the dynamic evolution characteristics of parameters in actual driving scenarios, this study captures the dynamic impact of the synergistic effect of altitude, ambient temperature, driving mode, and operating condition characteristics on emission temperature, the real-time regulation law of emission temperature on catalyst performance, and the change mechanism of emission energy consumption performance under the coupling effect of the two. It forms data communication and logical connection with the correlation model, providing quantitative support for analyzing the control logic correlation of engine emission after-treatment system under different application scenarios.

[0033] Finally, by using a correlation model to quantify the dynamic relationship between operating parameters and emissions and energy consumption, and combining this with an influence law model to clarify the effects of multiple factors, including the environment, on the system state, the two work together to provide complete quantitative support for analyzing the control logic of the "engine-emission aftertreatment" system. The analysis also examines the correlation relationships of the control logic of the "engine-emission aftertreatment" system under different application scenarios.

[0034] The parsing process of the control logic relationship is as follows: Assume that the core control parameters include the engine air-fuel ratio. Exhaust gas recirculation rate Regeneration threshold Characterization parameters of emission system heating strategy ; Based on instantaneous pollutant emissions Instantaneous energy consumption is Emission temperature affecting the output of the law model Catalyst performance indicators Comprehensive indicators of emission energy consumption performance ,altitude Ambient temperature Driving mode The time variable is For the input variables, construct a dynamic relationship between the control parameters and the input variables, satisfying: in, For the specific power of motor vehicles, The optimal operating temperature for the catalyst, Characterizing the synergistic cumulative effect between emission temperature and catalyst performance, reflecting the core correlation factor for air-fuel ratio regulation, This characterizes the cumulative impact of vehicle power demand on the exhaust gas recirculation rate. Characterizing the coupled cumulative effect of catalyst performance and emission temperature provides a quantitative basis for determining the regeneration threshold. Characterize the cumulative deviation between the emission temperature and the optimal operating temperature of the catalyst to clarify the direction of the control of the emission system heating strategy; , , , The mapping function is constructed based on the output data of the correlation model and the influence law model, combined with the engine combustion theory and the chemical reaction mechanism of the aftertreatment. Its input is the collaborative operation parameters of the engine and the aftertreatment system, which includes the combustion temperature, air-fuel ratio, EGR rate and fuel injection quantity on the engine side, and the catalyst bed temperature, exhaust flow rate, pollutant inlet concentration and reducing agent injection quantity on the aftertreatment system side. The function output is the engine combustion optimization and control parameters and the collaborative purification and adaptation parameters of the aftertreatment system. The construction process can be based on the flame propagation law and combustible mixture combustion reaction equation in engine combustion theory, as well as the catalytic reduction reaction formula and oxidation reaction kinetic equation in the aftertreatment chemical reaction mechanism. Combined with the publicly available output data dimensions and data preprocessing rules (including data cleaning, dimensionless transformation, and extreme value removal) of the correlation model and the influence law model, the mapping function is constructed through algorithms such as multivariate nonlinear regression. This set of mapping functions is mainly used for the coordinated optimization and control of engine combustion and aftertreatment system purification.

[0035] This set of mathematical relationships quantifies the dynamic causal relationships between core control parameters and operating conditions, environmental factors, system operating status, and output performance. It analyzes the adaptation mechanism of control parameter adjustment logic and system performance optimization goals under different application scenarios, forming a complete control logic association system, and providing quantitative support for subsequent adaptive optimization of control strategies.

[0036] (3) Database setup and strategy optimization After completing model building and control logic analysis, to ensure accurate adaptation of control strategies to multiple scenarios and emission standards, a unified data support system and a dynamic optimization mechanism need to be established. First, a comprehensive database of "environmental conditions, power parameters, catalyst parameters, and emission energy consumption" needs to be built. The core function of this database is to integrate the raw test data collected in the early stages with the quantitative relationship results formed by model analysis, breaking the limitations of scattered data storage and achieving centralized management of multi-dimensional data such as environmental characteristics, operating parameters, system status, and performance indicators. This provides complete and coherent data support for the subsequent design of optimization mechanisms, ensuring that the optimization process can be carried out based on a complete information system.

[0037] Based on this comprehensive database, and integrating the analytical results of the correlation model and the influence law model, an adaptive optimization mechanism for the electronic control system was further designed, constructing a dynamic control system based on real-time operating condition feedback. This mechanism collects environmental and operating condition characteristic data such as altitude, ambient temperature, and driving mode in real time during actual operation through the electronic control system. Combined with the dynamic characteristics of operating parameters and real-time monitoring data of emission temperature and catalyst performance, it then... Quantitative values ​​of the deviation between environmental operating conditions and standard operating conditions. ,in For altitude, For ambient temperature, Driving mode For speed, For acceleration, For emission temperature, For catalyst performance indicators, This is a deviation quantization function, which can be expressed as altitude. Ambient temperature Driving modes ,speed acceleration Emission temperature Catalyst performance indicators Using data as input, after data cleaning and normalization preprocessing, and based on benchmark data collected cyclically by, for example, WLTC, a multiple linear regression algorithm is used to construct a deviation quantification mapping relationship between actual operating conditions and standard operating conditions. Meanwhile, those skilled in the art can construct corresponding deviation quantification functions according to their specific implementation requirements.

[0038] Based on deviation quantization value ,pass Dynamically adjust the base model parameters (Covering atmospheric pressure calculation model parameters and air density calculation model parameters), among which For time variables, For integration time variable, Characterizing the cumulative effect of bias, This is the function for adjusting the basic model parameters; combined with the adjusted basic model parameters, the engine air-fuel ratio is optimized using the formulas below. Exhaust gas recirculation rate Regeneration threshold Characterization parameters of emission system heating strategy : in, Instantaneous pollutant emissions, For instantaneous energy consumption, For the specific power of motor vehicles, The optimal operating temperature for the catalyst; , , To control the parameter optimization function, its construction process first involves dynamically adjusting the basic model parameters. With real-time operating parameters (emission temperature) Catalyst performance indicators etc.), cumulative effect term ( , The system takes the following parameters as input and performs preprocessing: first, it cleans and dimensionlessly transforms the input data; second, based on engine combustion theory (such as the correlation between air-fuel ratio and combustion efficiency) and after-treatment chemical reaction mechanisms (such as the kinetic characteristics of catalyst activity and temperature), it determines the theoretical correlation between input parameters and output control quantities; finally, it uses algorithms such as gradient boosting trees to train the preprocessed data, constructing a dynamic mapping relationship between input and output, and determining the error range between the output air-fuel ratio, exhaust gas recirculation rate, regeneration threshold, and heating strategy characterization parameters and the measured optimal values. .

[0039] Through a closed-loop iteration of real-time data acquisition, deviation quantification, model adjustment, and parameter optimization, the electronic control system can dynamically adapt to different application scenarios and test conditions, ensuring that the "engine-aftertreatment" control strategy matches the actual operating conditions, while also taking into account the emission and energy consumption performance requirements of different emission standards.

[0040] The core function of this section is to dynamically adjust the core control parameters of the "engine-aftertreatment" system to adapt the system to different application scenarios, test conditions, and target emission standards. Specifically, this includes: Engine air-fuel ratio optimization: Based on the characteristics of actual environmental conditions and the performance of catalysts, the air-fuel mixing ratio is dynamically adjusted to ensure the rationality of engine combustion. While improving energy utilization efficiency, it reduces the amount of pollutants generated and provides favorable conditions for the conversion of pollutants in the after-treatment system. Optimization of exhaust gas recirculation rate: By combining driving mode, vehicle power demand and pollutant emission control targets, the proportion of exhaust gas recirculation to participate in combustion is adjusted to optimize engine combustion state, achieve a synergistic balance between power performance and pollutant emission reduction, and adapt to the limit requirements of different emission standards. Regeneration threshold optimization: Based on catalyst performance status, emission temperature changes and after-treatment system operation status, dynamically set the after-treatment system regeneration start-up conditions to avoid excessive emissions and unnecessary energy consumption, ensure stable operation of the after-treatment system, and ensure that pollutant conversion efficiency meets target regulatory requirements. Emission system heating strategy optimization: For application scenarios where the catalyst has difficulty reaching the optimal operating temperature quickly, the timing and intensity of heating start-up are adjusted to drive the catalyst temperature to quickly enter the optimal operating range, improve pollutant conversion efficiency, and ensure emission compliance under extreme environments; By optimizing any one of the above control parameters or combining multiple control parameters, the "engine-aftertreatment" control strategy can be dynamically adapted to different environmental conditions, driving modes, and test conditions, ensuring that the powertrain system meets the emission standards of the target export area while taking into account both energy economy and system operation stability.

[0041] (4) Simulation verification and scheme determination After completing the design of the adaptive optimization mechanism of the electronic control system and the adjustment of the core control parameters, in order to ensure that the optimized control strategy can adapt to the emission standards and actual operation requirements of the target area, targeted simulation development and real vehicle verification work needs to be carried out to form a complete development closed loop of "strategy optimization - simulation testing - parameter iteration - real vehicle verification".

[0042] First, by combining the clearly defined emission limits and specific test conditions of the target export region, and fully integrating the adaptation requirements of the previously optimized control strategy, a core basis is established for subsequent simulation model construction and testing. Using the optimized "engine-aftertreatment" control strategy as the core, and integrating core data from the previously established comprehensive database of "environmental conditions-power parameters-catalyst parameters-emissions and energy consumption," a full-dimensional powertrain system simulation model is constructed. This model must be able to accurately reproduce the test condition characteristics (such as operating speed and load variation patterns) corresponding to the emission standards of the target region, as well as actual operating environmental conditions (such as typical altitude and ambient temperature range), providing fundamental support for the realism and reliability of subsequent simulation tests.

[0043] Subsequently, compliance and performance simulation tests were conducted. The pollutant emission limits stipulated by the emission standards of the target area were used as the compliance judgment benchmark, and the vehicle's energy consumption economy was used as the core performance evaluation indicator. The test condition parameter sequences corresponding to the target area (such as speed-time curves, acceleration change sequences, etc.) and typical environmental characteristic parameters (such as average altitude, extreme temperature values, etc.) were input into the constructed powertrain system simulation model to simulate the entire process of engine combustion, pollutant conversion in the aftertreatment system, and vehicle energy consumption under different operating scenarios.

[0044] The simulation model outputs key data, including emission data for various pollutants (hydrocarbons, carbon monoxide, nitrogen oxides, particulate matter, etc.), instantaneous and cumulative energy consumption data for the entire vehicle, and system operating status data such as engine speed, emission temperature, and catalyst performance. The simulated pollutant emission data is compared item by item with the emission limits stipulated in the target area's emission standards to complete compliance verification. Simultaneously, energy consumption data is quantitatively analyzed to assess the energy efficiency of the optimized control strategy. Based on the verification and evaluation results, a simulation test report is generated, including compliance determination conclusions, performance evaluation data, and a detailed analysis of the system's operating status, providing a clear basis for any subsequent adjustments to the control strategy parameters.

[0045] If the simulation results do not meet the target regulatory requirements, i.e., the pollutant emission data output by the simulation does not meet the limit requirements of the emission standards of the target area, or the energy consumption performance does not meet expectations, it is necessary to combine the problems identified in the simulation test report, link them to the previous control strategy optimization steps, and return to the control strategy optimization steps to make targeted adjustments to the basic model parameters and core control parameters (such as engine air-fuel ratio, exhaust gas recirculation rate, etc.). After the adjustments are completed, the above simulation test process should be carried out again until the simulation results meet the standards.

[0046] Once the simulation test is successful, the optimized control strategy needs to be verified on a real vehicle. Typical operating scenarios and test conditions in the target area should be selected for real-vehicle road tests. Pollutant emission data, energy consumption data, and system operating parameters should be collected in real time during the actual vehicle operation. These data should be compared with the simulation test results to verify the actual compliance of emission and energy consumption indicators, ensuring that the control strategy can still stably meet the standards under real operating conditions.

[0047] Finally, based on the results of real vehicle verification, after confirming that the control strategy meets the emission standards and performance requirements of the target area, the final "engine-aftertreatment" control strategy matching scheme is formed, and the simulation development and verification process of the entire powertrain system is completed.

[0048] The powertrain simulation development and verification method proposed in this embodiment aligns with the core need for adapting to emission standards in multiple regions within the context of the current global automotive industry, and has broad application prospects. At the policy level, as emission regulations in various regions around the world become increasingly stringent and significantly differentiated, this method can quickly respond to the regulatory requirements of different target regions. Through a closed-loop process of "model building - strategy optimization - simulation verification," it significantly shortens the development cycle of powertrains for specific markets, reduces R&D rework costs caused by insufficient regulatory adaptation, and provides technical support for companies to seize opportunities in the international market. At the industry level, its core logic of integrating multi-dimensional data to build a comprehensive database and dynamically optimizing control strategies can be widely applied to the powertrain development of gasoline and hybrid vehicles, especially adapting to the customized R&D needs of export vehicles, helping companies improve the market competitiveness and compliance of their products.

[0049] Furthermore, the technical approach of this embodiment can be extended to the field of optimization and verification of new energy vehicle powertrain systems, providing a reference for the coordinated adaptation of motor control strategies, battery management systems, and emission-related auxiliary systems. Simultaneously, the integrated "data-model-strategy-verification" system formed by this method can promote the transformation of powertrain R&D from the traditional real-vehicle trial-and-error mode to a digital simulation mode, reducing the frequency of real-vehicle testing, lowering R&D energy consumption and environmental impact, and aligning with the green and low-carbon development trend of the automotive industry. In the future, with the integration of intelligent connected vehicle technologies, this method can further access real-time road condition data to achieve online iterative optimization of control strategies, providing continuous technical support for the dynamic adaptation of intelligent vehicle powertrain systems.

[0050] Example 2 Please refer to Figure 2 This embodiment 2 provides a simulation development and verification system for powertrains applicable to different emission standards, including: The data acquisition and standardization unit is used to determine the core benchmark parameters of the powertrain system and set test variables covering the environmental characteristics of the target application area; for test conditions corresponding to different emission standards, the powertrain system is tested, and operating condition parameters, pollutant emission data, energy consumption data and system operating status data are collected. The collected data is standardized and a raw database is established. The modeling and control logic parsing unit is used to construct a correlation model between operating parameters and emission and energy consumption data based on the standardized raw data; combined with test variables, it establishes a model of the influence of multiple factors, including environment and driving behavior, on emission temperature, catalyst performance, and emission energy consumption performance; and through the correlation model and the influence model, it analyzes the control logic correlation relationship of the "engine-emission aftertreatment" system under different application scenarios. The database construction and strategy optimization unit is used to build a comprehensive database of "environmental conditions - power parameters - catalyst parameters - emission energy consumption", integrating raw data and model analysis results. Based on this database, an adaptive optimization mechanism for the electronic control system is designed, enabling the electronic control system to dynamically adjust the basic model parameters according to the characteristics of the actual operating environment, thereby optimizing at least one of the engine air-fuel ratio, exhaust gas recirculation rate, regeneration threshold, and emission system heating strategy, so as to achieve the matching of the "engine-aftertreatment" control strategy with the application scenario and test conditions. The simulation verification and scheme determination unit is used to construct a powertrain system simulation model based on the optimized control strategy for the emission standards and regulations and corresponding test conditions of the target area, and to conduct compliance and performance simulation tests. If the simulation results do not meet the target regulatory requirements, the unit returns to the control strategy optimization step to adjust the parameters until the simulation meets the standards. The control strategy that meets the standards is then verified on a real vehicle to check the compliance of emission and energy consumption indicators, form the final matching scheme, and complete the development verification.

[0051] Example 3 This embodiment 3 also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement any step of a simulation development and verification method for a powertrain applicable to different emission standards.

[0052] The computer-readable storage medium may include 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.

[0053] For a description of the computer-readable storage medium provided in this application, please refer to the above method embodiments; further details will not be repeated here.

[0054] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for simulation development and validation of powertrains for different emission standards, characterized in that, include: S1. Determine the core baseline parameters of the powertrain system and set test variables that cover the environmental characteristics of the target application area; For test conditions corresponding to different emission standards, powertrain system tests are carried out, and operating parameters, pollutant emission data, energy consumption data and system operation status data are collected. The collected data are standardized and a raw database is established. S2. Based on the standardized raw data, construct a correlation model between operating parameters and emission and energy consumption data; combine test variables to establish a model of the influence of multiple factors, including environment and driving behavior, on emission temperature, catalyst performance, and emission energy consumption performance; through the correlation model and the influence model, analyze the control logic correlation of the "engine-emission aftertreatment" system under different application scenarios. S3. Establish a comprehensive database of "environmental conditions - power parameters - catalyst parameters - emission energy consumption" and integrate raw data with model analysis results; based on this database, design an adaptive optimization mechanism for the electronic control system, enabling the electronic control system to dynamically adjust the basic model parameters according to the characteristics of the actual operating environment, thereby optimizing at least one of the engine air-fuel ratio, exhaust gas recirculation rate, regeneration threshold, and emission system heating strategy, so as to achieve the matching of the "engine-aftertreatment" control strategy with the application scenario and test conditions; S4. Based on the emission standards and regulations of the target area and the corresponding test conditions, construct a powertrain system simulation model based on the optimized control strategy, and conduct compliance and performance simulation tests. If the simulation results do not meet the target regulatory requirements, return to the control strategy optimization step to adjust the parameters until the simulation meets the standards. Verify the control strategy after compliance with the standards on a real vehicle, check the compliance of emission and energy consumption indicators, form the final matching scheme, and complete the development verification.

2. The method of claim 1, wherein, The test variables in S1 include at least one of altitude, ambient temperature, and driving mode.

3. The method of claim 1, wherein, The association model in S2 is specifically as follows: Let the instantaneous speed under the test working condition be , the instantaneous acceleration be , the specific power of the motor vehicle be , the vehicle mass be , and the road slope be , the following equation is derived ; wherein is the gravitational acceleration, is the rolling resistance coefficient, and is the road slope. by , , As the core input variable, the model output is the instantaneous pollutant emissions. With instantaneous energy consumption Their mathematical relationship satisfies: in, For time variables, It characterizes the cumulative dynamic features of operating parameters, reflecting the cumulative changes in vehicle power demand per unit time. It represents the cumulative effect of the vehicle's power output, which corresponds to the lagging impact of the continuous accumulation of power demand during actual driving on emissions and energy consumption. , This is a dynamic mapping function built based on standardized raw data.

4. The simulation development and verification method for powertrains applicable to different emission standards according to claim 3, characterized in that, The specific influence law model in S2 is as follows: Let the altitude be The ambient temperature is Driving mode is The dynamic characteristics of the operating parameters are as follows: ,by , , , As a core input variable; The model output is the emission temperature. Catalyst performance indicators Comprehensive indicators of emission energy consumption performance Their mathematical relationship satisfies: in, For time variables, For integration time variable, It characterizes the cumulative effect of the vehicle's power demand and reflects the continuous effect of engine load under different operating conditions. The cumulative effect of emission temperature is characterized, which aligns with the dynamic change in catalyst activity over time. The time-cumulative effect characterizing catalyst performance reflects its sustained impact on emissions and energy consumption; , , It is a mapping function constructed based on heat and mass transfer theory, chemical reaction kinetics principles, and standardized raw data.

5. The simulation development and verification method for powertrains applicable to different emission standards according to claim 4, characterized in that, The parsing process of the control logic association in S2 is as follows: Assume the core control parameters include the engine air-fuel ratio. Exhaust gas recirculation rate Regeneration threshold Characterization parameters of emission system heating strategy ; Based on instantaneous pollutant emissions Instantaneous energy consumption is Emission temperature affecting the output of the law model Catalyst performance indicators Comprehensive indicators of emission energy consumption performance ,altitude Ambient temperature Driving mode The time variable is For the input variables, construct a dynamic relationship between the control parameters and the input variables, satisfying: in, For the specific power of motor vehicles, The optimal operating temperature for the catalyst, Characterizing the synergistic cumulative effect between emission temperature and catalyst performance, reflecting the core correlation factor for air-fuel ratio regulation, This characterizes the cumulative impact of vehicle power demand on the exhaust gas recirculation rate. Characterizing the coupled cumulative effect of catalyst performance and emission temperature provides a quantitative basis for determining the regeneration threshold. Characterize the cumulative deviation between the emission temperature and the optimal operating temperature of the catalyst to clarify the direction of the control of the emission system heating strategy; , , , It is a mapping function constructed based on the output data of the correlation model and the influence law model, combined with engine combustion theory and after-treatment chemical reaction mechanism.

6. The simulation development and verification method for powertrains applicable to different emission standards according to claim 1, characterized in that, The adaptive optimization mechanism in S3 is specifically as follows: This mechanism collects real-time environmental and operating condition data during actual operation through the electronic control system, combining this data with dynamic characteristics of operating parameters and real-time monitoring data of emission temperature and catalyst performance. Quantitative values ​​of the deviation between environmental operating conditions and standard operating conditions. ,in For altitude, For ambient temperature, Driving mode For speed, For acceleration, For emission temperature, For catalyst performance indicators, This is the deviation quantization function; Based on deviation quantization value ,pass Dynamically adjust the base model parameters ,in For time variables, For integration time variable, Characterizing the cumulative effect of bias, This is the function for adjusting the basic model parameters; combined with the adjusted basic model parameters, the engine air-fuel ratio is optimized using the formulas below. Exhaust gas recirculation rate Regeneration threshold Characterization parameters of emission system heating strategy : in, Instantaneous pollutant emissions, For instantaneous energy consumption, For the specific power of motor vehicles, The optimal operating temperature for the catalyst, , , , This is a function for optimizing control parameters.

7. The simulation development and verification method for powertrains applicable to different emission standards according to claim 1, characterized in that, The process of further optimizing at least one of the engine air-fuel ratio, exhaust gas recirculation rate, regeneration threshold, and emission system heating strategy in S3 specifically includes: Its core function is to adapt the system to different application scenarios, test conditions, and target emission standards by dynamically adjusting the core control parameters of the "engine-aftertreatment" system. Specifically, this includes: Engine air-fuel ratio optimization: Based on the characteristics of actual environmental conditions and the performance of catalysts, the air-fuel mixing ratio is dynamically adjusted to ensure the rationality of engine combustion. While improving energy utilization efficiency, it reduces the amount of pollutants generated and provides favorable conditions for the conversion of pollutants in the after-treatment system. Optimization of exhaust gas recirculation rate: By combining driving mode, vehicle power demand and pollutant emission control targets, the proportion of exhaust gas recirculation to participate in combustion is adjusted to optimize engine combustion state, achieve a synergistic balance between power performance and pollutant emission reduction, and adapt to the limit requirements of different emission standards. Regeneration threshold optimization: Based on catalyst performance status, emission temperature changes and after-treatment system operation status, dynamically set the after-treatment system regeneration start-up conditions to avoid excessive emissions and unnecessary energy consumption, ensure stable operation of the after-treatment system, and ensure that pollutant conversion efficiency meets target regulatory requirements. Emission system heating strategy optimization: For application scenarios where the catalyst has difficulty reaching the optimal operating temperature quickly, the timing and intensity of heating start-up are adjusted to drive the catalyst temperature to quickly enter the optimal operating range, improve pollutant conversion efficiency, and ensure emission compliance under extreme environments; By optimizing any one of the above control parameters or combining multiple control parameters, the "engine-aftertreatment" control strategy can be dynamically adapted to different environmental conditions, driving modes, and test conditions, ensuring that the powertrain system meets the emission standards of the target export area while taking into account both energy economy and system operation stability.

8. The simulation development and verification method for powertrains applicable to different emission standards according to claim 1, characterized in that, The process of conducting compliance and performance simulation testing in S4 is as follows: Based on the optimized engine after-treatment control strategy, a powertrain system simulation model is constructed by integrating core data from the comprehensive database. This model is used to reproduce the test conditions and actual operating environment conditions corresponding to the emission standards of the target area. Using the emission limits of the target area as the compliance judgment benchmark and energy consumption economy as the performance evaluation index, the test condition parameter sequence and the environmental characteristic parameters of the target area are input into the simulation model to simulate the operation of the engine after-treatment system and the entire process of vehicle energy consumption under different operating scenarios. The simulation model outputs pollutant emission data, vehicle energy consumption data, and system operating status data. Compliance verification is completed by comparing pollutant emission data with the limits specified in the emission standards of the target area, and performance evaluation is completed by quantitative analysis of energy consumption data. A simulation test report is generated, which includes compliance judgment results, performance evaluation results and system operation status analysis, to provide a basis for subsequent adjustment of control strategy parameters.

9. A simulation development and verification system for powertrains applicable to different emission standards, characterized in that, include: The data acquisition and standardization unit is used to determine the core benchmark parameters of the powertrain system and set test variables that cover the environmental characteristics of the target application area; For test conditions corresponding to different emission standards, powertrain system tests are carried out, and operating parameters, pollutant emission data, energy consumption data and system operation status data are collected. The collected data are standardized and a raw database is established. The modeling and control logic parsing unit is used to construct a correlation model between operating parameters and emission and energy consumption data based on the standardized raw data; combined with test variables, it establishes a model of the influence of multiple factors, including environment and driving behavior, on emission temperature, catalyst performance, and emission energy consumption performance; and through the correlation model and the influence model, it analyzes the control logic correlation relationship of the "engine-emission aftertreatment" system under different application scenarios. The database construction and strategy optimization unit is used to build a comprehensive database of "environmental conditions - power parameters - catalyst parameters - emission energy consumption", integrating raw data and model analysis results. Based on this database, an adaptive optimization mechanism for the electronic control system is designed, enabling the electronic control system to dynamically adjust the basic model parameters according to the characteristics of the actual operating environment, thereby optimizing at least one of the engine air-fuel ratio, exhaust gas recirculation rate, regeneration threshold, and emission system heating strategy, so as to achieve the matching of the "engine-aftertreatment" control strategy with the application scenario and test conditions. The simulation verification and scheme determination unit is used to construct a powertrain system simulation model based on the optimized control strategy for the emission standards and regulations and corresponding test conditions of the target area, and to conduct compliance and performance simulation tests. If the simulation results do not meet the target regulatory requirements, the unit returns to the control strategy optimization step to adjust the parameters until the simulation meets the standards. The control strategy that meets the standards is then verified on a real vehicle to check the compliance of emission and energy consumption indicators, form the final matching scheme, and complete the development verification.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is executed by a processor as described in any one of claims 1-8, which is a simulation development and verification method for powertrains applicable to different emission standards.

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