A vehicle WLTC working condition multi-pollutant emission prediction method and system based on limited characteristic temperature points

Abstract

The application discloses a vehicle WLTC working condition multi-pollutant emission prediction method and system based on limited characteristic temperature points, relates to the vehicle pollutant monitoring technical field, and the method comprises the following steps: WLTC working condition cycle test is carried out at limited characteristic temperature points, and the unit mileage emission intensity of each speed stage is obtained.Combining the thermal state attenuation coefficient and the driving mileage, the equivalent emission amount and the relative emission factor are calculated.According to the emission sensitivity index, the segmented boundary temperature is adaptively adjusted, the optimal segmented temperature is determined, and the temperature-emission response model of the low-temperature and high-temperature zones is respectively established.The transition width is calculated based on the sensitivity index, the two models are fused by constructing a smoothing weight function, and the prediction relative emission factor under any temperature is obtained.Finally, the emission prediction value under the target temperature and humidity is obtained by multiplying the environmental humidity correction factor and the benchmark temperature equivalent emission amount.

Description

Technical Field

[0001] This invention belongs to the field of vehicle pollutant monitoring technology, and more specifically, relates to a method and system for predicting multi-pollutant emissions from vehicles under WLTC conditions based on a limited number of characteristic temperature points. Background Technology

[0002] The current China VI emission standard (GB 18352.6-2016) stipulates that type testing of light-duty vehicles must be conducted in a standard room temperature environment of 23℃±5℃ under WLTC conditions. However, the actual operating environment temperature ranges extremely wide, from below -30℃ in northern winters to above 40℃ in southern summers. This requires automakers to systematically verify the emission compliance of vehicles over a wide temperature range during the product development stage.

[0003] Therefore, companies typically need to use specialized temperature-controlled environmental chambers to complete the WLTC full-cycle test under multiple temperature conditions (such as -25℃, 0℃, 23℃, and 40℃). The cost of a single temperature-controlled environmental chamber test ranges from tens of thousands to hundreds of thousands of yuan, and the total cost of a complete set of multi-temperature point tests often exceeds one million yuan, with a testing cycle lasting several weeks.

[0004] The existing technology has the following four shortcomings: First, it ignores the differences in engine thermal evolution at different speed stages within the WLTC cycle. Existing methods collect emissions data for the WLTC cycle as a whole, failing to incorporate the differences in cold start thermal effects at low, medium, high, and ultra-high speed stages into data processing, resulting in an insufficient physical basis for equivalent emissions.

[0005] Second, a uniform and fixed segment boundary temperature is adopted for all pollutants. Existing methods uniformly use 23℃ as the segment boundary between the low-temperature and high-temperature regions of the model, without considering the differences in the temperature response characteristics of CO, THC, NMHC, and CO2, resulting in significant prediction biases for some pollutants near the segment boundary.

[0006] Third, the sigmoid smoothing parameter remains constant, lacking the ability to adaptively adjust to the temperature sensitivity of pollutants. Existing methods consistently use K=0.8 as the transition width parameter of the sigmoid function, treating all pollutants uniformly and failing to reflect the differences in the shape of emission curves for different pollutants.

[0007] Fourth, the impact of ambient humidity on emission prediction results is not considered. Existing methods only use temperature as a variable and do not incorporate an environmental humidity correction mechanism that has a significant impact on CO and hydrocarbon emissions, thus limiting the prediction accuracy of the method under non-standard humidity environments.

[0008] Therefore, there is an urgent need for a method that can accurately predict the emissions of multiple pollutants under WLTC conditions over a wide temperature range with fewer tests, more comprehensive physical mechanism basis, and greater adaptability. Summary of the Invention

[0009] To address the above technical problems, this invention proposes a method for predicting multi-pollutant emissions from vehicles under WLTC conditions based on a finite number of characteristic temperature points, comprising: The WLTC cycle test was completed on the vehicle under test at a limited number of characteristic temperature points. At each characteristic temperature point, emission data of each pollutant and driving mileage were collected at each speed stage of the WLTC cycle to obtain the emission intensity per unit mileage at each speed stage. The phase thermal decay coefficients at each speed stage under each characteristic temperature point are constructed, and the emission intensity per unit mileage at each speed stage is comprehensively processed in combination with the driving mileage to obtain the equivalent emission of each pollutant at each characteristic temperature point, and the relative emission factor at each characteristic temperature point is calculated. The emission sensitivity index of each pollutant is calculated based on the relative emission factor bias. Based on the emission sensitivity index, the segment boundary temperature of each pollutant is adaptively adjusted to determine the optimal segment temperature of each pollutant. The dynamic self-evolving segment boundary temperature is calculated based on the optimal segment temperature and used as the segment boundary. A first temperature-emission response model is constructed in the low-temperature region, and a second temperature-emission response model is constructed in the high-temperature region. The adaptive transition width parameters of each pollutant are calculated based on the emission sensitivity index, and a smooth transition weight function is constructed. The first temperature-emission response model and the second temperature-emission response model are then fused to obtain the predicted relative emission factors of each pollutant at any temperature. The predicted emissions of each pollutant at the target temperature are obtained by multiplying the predicted relative emission factor and the equivalent emissions at the baseline temperature.

[0010] Furthermore, the calculation of the phase hot-state decay coefficient includes: in, For the first Characteristic temperature points Next Phase thermal decay coefficient at each velocity stage For the first Cold start thermal impact factors at each speed stage The normalized decay constant for hot recovery. This is the lowest characteristic temperature point. This is the point with the highest characteristic temperature.

[0011] Furthermore, the equivalent emissions of each pollutant at each characteristic temperature point are obtained, including: in, pollutants In the Characteristic temperature points The equivalent emissions below For the first Mileage at each speed level pollutants In the Characteristic temperature points Next Emission intensity per unit mileage at each speed stage pollutants In the Characteristic temperature points The residual thermal inertia memory factor; Calculate pollutants In the Characteristic temperature points Thermal inertia residual memory factor include: in, This is the thermal inertia amplification factor. In the first Characteristic temperature points Next The equivalent thermal energy storage density of the engine at the end of each speed phase For reference thermal energy storage density, This is the thermal decay time coefficient. Thermal inertia nonlinear index For the first The equivalent thermal interval time before the start of each velocity phase. This is the reference time constant.

[0012] Furthermore, the calculation of the relative emission factor at each characteristic temperature point includes: in, pollutants In the Characteristic temperature points The relative emission factor under the following conditions pollutants At standard reference temperature The equivalent emissions below; The calculation of the emission sensitivity index for each pollutant includes: in, pollutants Emission sensitivity index As the first weight of the emissions sensitivity index, pollutants At the lowest characteristic temperature point The relative emission factor under the following conditions As the second weight of the emissions sensitivity index, pollutants Cold start characteristic temperature The relative emission factor.

[0013] Furthermore, determining the optimal segmented temperature for each pollutant includes: in, pollutants The optimal segmented temperature, This represents the maximum segmented temperature adjustment range. This is the arithmetic mean of the emission sensitivity indices for all pollutants.

[0014] Furthermore, it also includes calculating the dynamic self-evolving segment boundary temperature based on the optimal segment temperature: in, For dynamically evolving segmented boundary temperatures, The curvature response modulation coefficient, The mean of the emission curvature entropy exponent for all pollutants. pollutants The emission curvature entropy index, This refers to the amplitude of dynamic boundary oscillations. Dynamically self-evolving segmented boundary temperature As segment boundaries.

[0015] Furthermore, the first temperature-emission response model includes: in, Pollutants in the low-temperature zone At the target temperature The predicted relative emission factor is as follows. This is the leading factor for the low temperature index. The low-temperature index decay rate, This is the low-temperature reference bias term; The second temperature-emission response model includes: in, Pollutants in high-temperature zones At the target temperature The predicted relative emission factor is as follows. This is a secondary correction factor for high temperature. This is the primary correction factor for high temperature.

[0016] Furthermore, adaptive transition width parameters for each pollutant are calculated based on the emission sensitivity index, and a smooth transition weighting function is constructed, including: Calculate the adaptive transition width parameters for each pollutant: in, pollutants The adaptive transition width parameter, As the reference transition width parameter, For adaptive modulation coefficients; Construct a smooth transition weight function: in, Target temperature The value of the smooth transition weight function.

[0017] Furthermore, the predicted relative emission factors for each pollutant at any temperature are obtained as follows: in, pollutants At the target temperature The predicted relative emission factor is as follows.

[0018] This invention also proposes a vehicle WLTC multi-pollutant emission prediction system based on finite characteristic temperature points, comprising: The data acquisition module is used to complete the WLTC cycle test of the vehicle under test at a limited number of characteristic temperature points. At each characteristic temperature point, the emission data of each pollutant and the driving mileage are collected at each speed stage of the WLTC cycle to obtain the emission intensity per unit mileage at each speed stage. The module for calculating relative emission factors is used to construct the phase thermal decay coefficient of each speed stage at each characteristic temperature point, and to comprehensively process the emission intensity per unit mileage at each speed stage in combination with the driving mileage, so as to obtain the equivalent emission of each pollutant at each characteristic temperature point and calculate the relative emission factor at each characteristic temperature point. The module for determining the optimal segmented temperature is used to calculate the emission sensitivity index of each pollutant based on the relative emission factor bias, and to adaptively adjust the segmented boundary temperature of each pollutant based on the emission sensitivity index to determine the optimal segmented temperature of each pollutant. The model setting module is used to calculate the dynamic self-evolving segment boundary temperature based on the optimal segment temperature, and use it as the segment boundary to build a first temperature-emission response model in the low temperature region and a second temperature-emission response model in the high temperature region. The module for calculating the predicted relative emission factor is used to calculate the adaptive transition width parameter of each pollutant based on the emission sensitivity index, and to construct a smooth transition weight function to fuse the first temperature-emission response model and the second temperature-emission response model to obtain the predicted relative emission factor of each pollutant at any temperature. The prediction module is used to obtain the predicted emission values ​​of each pollutant at the target temperature by multiplying the predicted relative emission factor and the equivalent emission at the reference temperature.

[0019] Overall, such as Figure 4 As shown, the above technical solutions conceived by this invention have the following beneficial effects compared with the prior art: 1. Significantly reduced testing costs. Only one WLTC stage test is required at each of the four characteristic temperature points to predict WLTC emissions across the entire temperature range of -30°C to 45°C. Compared to the traditional approach that requires testing under more than eight temperature conditions, this is expected to save over 65% on temperature-controlled environmental chamber testing costs and shorten the product development and verification cycle by 4 to 6 weeks.

[0020] 2. More complete physical mechanism. The difference in cold start impact at different velocity stages of WLTC is quantitatively characterized by the phase thermal decay coefficient. A phase weighted equivalent emission with more sufficient physical basis than the total cycle method is constructed, which improves the representativeness of the baseline data and the rationality of the model mechanism.

[0021] 3. High-precision adaptive prediction. By using the emission sensitivity index to drive the pollutant-specific optimal segmented temperature and adaptive Sigmoid transition width parameter, the limitations of fixed segment boundaries and fixed transition parameters on the prediction accuracy of different pollutants are overcome. The relative prediction errors of CO, THC, NMHC and CO2 across the entire temperature range do not exceed 13%.

[0022] 4. High versatility. This method does not depend on a specific engine type or after-treatment technology and is applicable to the prediction of CO, THC, NMHC, and CO2 pollutant emissions from gasoline vehicles, diesel vehicles, and hybrid light vehicles under WLTC conditions. Attached Figure Description

[0023] Figure 1 This is a flowchart of the method in Embodiment 1 of the present invention; Figure 2 This is a system structure diagram of Embodiment 2 of the present invention; Figure 3 This is a system structure diagram of Embodiment 4 of the present invention; Figure 4 This is a comparison diagram of the effects of the present invention and the traditional solution. Detailed Implementation

[0024] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0025] The method provided by this invention can be implemented in a terminal environment that may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, which is loaded and executed by the processor to implement the method described in the following embodiments.

[0026] A processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts of the terminal, and performs various functions and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and by calling data stored in the storage medium.

[0027] Storage media can include random access memory (RAM) or read-only memory (ROM). Storage media can be used to store instructions, programs, code, code sets, or instructions.

[0028] The display screen is used to show the user interface of each application.

[0029] In addition, those skilled in the art will understand that the structure of the terminal described above does not constitute a limitation on the terminal. The terminal may include more or fewer components, or combine certain components, or have different component arrangements. For example, the terminal may also include radio frequency circuits, input units, sensors, audio circuits, power supplies, and other components, which will not be described in detail here.

[0030] Example 1 like Figure 1 As shown, this embodiment proposes a multi-pollutant emission prediction method for vehicles under WLTC conditions based on finite characteristic temperature points, including: Step 101: Complete the WLTC cycle test on the vehicle under test at a limited number of characteristic temperature points. At each characteristic temperature point, collect emission data and driving mileage for each pollutant at each speed stage of the WLTC cycle to obtain the emission intensity per unit mileage at each speed stage. Preferably, the ambient temperature is set as four characteristic temperature points on the chassis dynamometer: in, The lowest characteristic temperature (°C). The characteristic temperature for cold start (°C). The standard reference temperature (°C) is used. The highest characteristic temperature (°C).

[0031] At each characteristic temperature point, complete at least one full WLTC cycle test according to the WLTC cycle procedure. The WLTC cycle consists of four speed stages, with the following mileage for each stage: In this context, the subscript L represents the low phase, M represents the medium phase, H represents the high phase, and EH represents the extra-high phase. ( L, M, H, EH ) represents the distance traveled at each speed stage (km); Total mileage (km) for the WLTC cycle.

[0032] For each pollutant ( CO, THC, NMHC, CO2 ), at the characteristic temperature point ( At each velocity stage, the total mass of emissions was collected. (mg). Calculate the emission intensity per unit mileage for each speed stage using the following formula. (mg / km): in, pollutants At the characteristic temperature point Next Emission intensity per unit mileage (mg / km) at each speed stage. pollutants At the characteristic temperature point Next Total mass of emissions (mg) at each speed stage For the first Distance traveled at each speed level (km).

[0033] Step 102: Construct the phase thermal decay coefficient of each speed stage at each characteristic temperature point, and combine the driving mileage to comprehensively process the emission intensity per unit mileage at each speed stage to obtain the equivalent emission of each pollutant at each characteristic temperature point, and calculate the relative emission factor at each characteristic temperature point. Specifically, as the engine gradually establishes its hot state after a cold start, the degree to which the cold start affects each speed stage increases as the ambient temperature decreases and decreases as the stage number increases. To quantitatively characterize this physical law, we focus on characteristic temperature points. The WLTC For each velocity stage, the phase hot-state decay coefficient is defined, and the calculation of the phase hot-state decay coefficient includes: in, For the first Characteristic temperature points Next Phase thermal decay coefficient at each velocity stage The larger the value, the greater the impact of cold start on this stage. For the first The cold start thermal impact factor for each speed stage, with values ​​of [value missing]. , , , At low speeds, the engine's thermal state is not yet fully established, therefore... maximum, The normalized decay constant for hot-state recovery characterizes the rate of engine hot-state recovery as temperature increases, for example... , This is the lowest characteristic temperature point. This is the point with the highest characteristic temperature.

[0034] Specifically, the equivalent emissions of each pollutant at each characteristic temperature point include: in, pollutants In the Characteristic temperature points The equivalent emissions below For the first Mileage at each speed level pollutants In the Characteristic temperature points Next Emission intensity per unit mileage at each speed stage pollutants In the Characteristic temperature points The residual thermal inertia memory factor; Traditional methods only consider the impact of ambient temperature on cold start hot-state recovery, but fail to account for the continued impact of residual thermal inertia energy generated by the engine in the previous stage of operation on emissions in subsequent stages. Therefore, this embodiment introduces a residual thermal inertia memory factor to calculate pollutant emissions. In the Characteristic temperature points Thermal inertia residual memory factor include: in, This is the thermal inertia amplification factor. In the first Characteristic temperature points Next The equivalent thermal energy storage density of the engine at the end of each speed phase For reference thermal energy storage density, This is the thermal decay time coefficient. Thermal inertia nonlinear index For the first The equivalent thermal interval time before the start of each velocity phase. This is the reference time constant.

[0035] Specifically, based on the standard reference temperature Based on this, the relative emission factors for each characteristic temperature point are calculated as follows: in, pollutants In the Characteristic temperature points The relative emission factor under the following conditions pollutants At standard reference temperature The equivalent emissions below.

[0036] Step 103: Calculate the emission sensitivity index of each pollutant based on the relative emission factor bias, and adaptively adjust the segment boundary temperature of each pollutant based on the emission sensitivity index to determine the optimal segment temperature of each pollutant. Specifically, to quantitatively describe the response of each pollutant's emissions to an ambient temperature deviation of 23°C, the emission sensitivity index for each pollutant was calculated, including: in, pollutants The emission sensitivity index (°C⁻¹) indicates that the higher the value, the more sensitive the pollutant emission is to changes in low temperature. As the first weight of the emission sensitivity index, for example , pollutants At the lowest characteristic temperature point The relative emission factor under the following conditions As the second weight of the emissions sensitivity index, for example , pollutants Cold start characteristic temperature The relative emission factor.

[0037] Specifically, existing methods generally use 23°C as the segment boundary between the low-temperature and high-temperature zones in the model. This invention uses the emission sensitivity index of each pollutant as the driving variable to calculate the optimal segment temperature specific to each pollutant. (°C) to accurately capture the morphological differences in emission curves of different pollutants. Specifically, determining the optimal segmented temperature for each pollutant includes: in, pollutants The optimal segmented temperature, For example, the maximum segmented temperature adjustment range. , This is the arithmetic mean of the emission sensitivity indices for all pollutants.

[0038] The significance lies in: pollutants that are more sensitive to temperature changes ( Its low-temperature nonlinear emission region extends towards high-temperature areas, and the optimal segment temperature shifts accordingly towards the high-temperature side; pollutants that are not sensitive to temperature changes ( The segmented temperatures shift towards the lower temperature side.

[0039] Step 104: Calculate the dynamic self-evolutionary segment boundary temperature based on the optimal segment temperature, and use it as the segment boundary in the low-temperature region ( Construct a first temperature-emission response model in the high-temperature region ( Construct a second temperature-emission response model; Specifically, to achieve more accurate temperature segmentation, this embodiment introduces a curvature entropy-driven mechanism based on the original optimal segmentation temperature to calculate the dynamically evolving segmentation boundary temperature: in, For dynamically evolving segmented boundary temperatures, The curvature response modulation coefficient, The mean of the emission curvature entropy exponent for all pollutants. pollutants The emission curvature entropy index, This refers to the amplitude of dynamic boundary oscillations. Dynamically self-evolving segmented boundary temperature As segment boundaries.

[0040] Preferably, calculate pollutants Emission curvature entropy index include: in, The total number of characteristic temperature points. pollutants In the Characteristic temperature points The relative emission factor under the following conditions pollutants In the Characteristic temperature points The relative emission factor under the following conditions This is the nonlinear amplification index of curvature.

[0041] Specifically, the first temperature-emission response model includes: in, Pollutants in the low-temperature zone At the target temperature The predicted relative emission factor is as follows. This is the leading factor for the low temperature index. The low-temperature exponential decay rate (°C⁻¹) is the rate of temperature decay. This is the low-temperature reference bias term. ,parameter and pass , , The relative emission factor values ​​at the three characteristic temperature points, in order to The result is obtained by nonlinear least squares fitting after recalibration based on the baseline.

[0042] The second temperature-emission response model includes: in, Pollutants in high-temperature zones At the target temperature The predicted relative emission factor is as follows. This is the high-temperature secondary correction factor (°C⁻²). This is the first-order correction factor for high temperature (°C⁻¹).

[0043] To ensure the dynamic self-evolution segment boundary temperature The first derivative of the temperature-emission response model is continuous on both sides, so that the second temperature-emission response model is... The first derivative at that point is equal to the first temperature-emission response model at... The first derivative at: Depend on relative emission factor value at Substitute into the second temperature-emission response model to solve: Step 105: Calculate the adaptive transition width parameter of each pollutant based on the emission sensitivity index, construct a smooth transition weight function, and fuse the first temperature-emission response model and the second temperature-emission response model to obtain the predicted relative emission factor of each pollutant at any temperature. Specifically, the adaptive transition width parameters for each pollutant are calculated based on the emission sensitivity index, and a smooth transition weighting function is constructed, including: Calculate the adaptive transition width parameters for each pollutant: in, pollutants The adaptive transition width parameter (°C⁻¹) has a larger value, which indicates a narrower fusion transition interval (a steeper transition). For example, the reference transition width parameter. , For adaptive modulation coefficients, for example ; The significance lies in: pollutants that are more sensitive to temperature changes ( The emission curve changes more drastically near the segmented temperatures, requiring a steeper transition function (larger). To accurately describe; pollutants that are insensitive to temperature changes ( ), using a smoother transition (smaller) ).

[0044] Construct a smooth transition weight function: in, Target temperature The smooth transition weight function value, ,when hour (The first temperature-emission response model dominates in the low-temperature region), when hour (The second temperature-emission response model dominates in the high-temperature zone).

[0045] Specifically, the predicted relative emission factors for each pollutant at any temperature include: in, pollutants At the target temperature The predicted relative emission factor is as follows.

[0046] Step 106: Multiply the predicted relative emission factor and the equivalent emission at the baseline temperature to obtain the predicted emission values ​​of each pollutant at the target temperature.

[0047] Example 2 like Figure 2 As shown, this embodiment proposes a vehicle WLTC multi-pollutant emission prediction system based on finite characteristic temperature points, including: The data acquisition module is used to complete the WLTC cycle test of the vehicle under test at a limited number of characteristic temperature points. At each characteristic temperature point, the emission data of each pollutant and the driving mileage are collected at each speed stage of the WLTC cycle to obtain the emission intensity per unit mileage at each speed stage. The module for calculating relative emission factors is used to construct the phase thermal decay coefficient of each speed stage at each characteristic temperature point, and to comprehensively process the emission intensity per unit mileage at each speed stage in combination with the driving mileage, so as to obtain the equivalent emission of each pollutant at each characteristic temperature point and calculate the relative emission factor at each characteristic temperature point. The module for determining the optimal segmented temperature is used to calculate the emission sensitivity index of each pollutant based on the relative emission factor bias, and to adaptively adjust the segmented boundary temperature of each pollutant based on the emission sensitivity index to determine the optimal segmented temperature of each pollutant. The model setting module is used to calculate the dynamic self-evolving segment boundary temperature based on the optimal segment temperature, and use it as the segment boundary to build a first temperature-emission response model in the low temperature region and a second temperature-emission response model in the high temperature region. The module for calculating the predicted relative emission factor is used to calculate the adaptive transition width parameter of each pollutant based on the emission sensitivity index, and to construct a smooth transition weight function to fuse the first temperature-emission response model and the second temperature-emission response model to obtain the predicted relative emission factor of each pollutant at any temperature. The prediction module is used to obtain the predicted emission values ​​of each pollutant at the target temperature by multiplying the predicted relative emission factor and the equivalent emission at the reference temperature.

[0048] Since Example 2 is based on Example 1, it will not be described again.

[0049] Example 3 This invention also proposes a storage medium storing multiple instructions for implementing the aforementioned method for predicting multi-pollutant emissions under WLTC conditions for vehicles based on finite characteristic temperature points.

[0050] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0051] Optionally, in this embodiment, the storage medium is configured to store program code for performing the method steps of Embodiment 1.

[0052] Example 4 This invention also proposes an electronic device, such as... Figure 3 As shown, it includes a processor and a storage medium connected to the processor. The storage medium stores multiple instructions that can be loaded and executed by the processor to enable the processor to execute the aforementioned method for predicting multiple pollutant emissions under WLTC conditions for vehicles based on finite characteristic temperature points.

[0053] Specifically, the electronic device in this embodiment can be a computer terminal, which may include one or more processors and a storage medium.

[0054] The storage medium can be used to store software programs and modules, such as the vehicle WLTC multi-pollutant emission prediction method based on finite characteristic temperature points in this embodiment of the invention. The processor executes the software programs and modules stored in the storage medium to perform various functional applications and data processing, thus realizing the aforementioned vehicle WLTC multi-pollutant emission prediction method based on finite characteristic temperature points. The storage medium may include high-speed random access storage media, and may also include non-volatile storage media, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include storage media remotely configured relative to the processor, which can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0055] The processor can execute the method steps of Embodiment 1 by calling the information and application stored in the storage medium through the transmission system.

[0056] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0057] In the several embodiments provided by this invention, it should be understood that the disclosed technical content can be implemented in other ways. The system embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between units or modules, and may be electrical or other forms.

[0058] 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0059] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0060] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it 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 all or 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: USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, optical disks, and other media capable of storing program code.

[0061] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for predicting multi-pollutant emissions from vehicles under WLTC conditions based on finite characteristic temperature points, characterized in that, include: The WLTC cycle test was completed on the vehicle under test at a limited number of characteristic temperature points. At each characteristic temperature point, emission data of each pollutant and driving mileage were collected at each speed stage of the WLTC cycle to obtain the emission intensity per unit mileage at each speed stage. The phase thermal decay coefficients at each speed stage under each characteristic temperature point are constructed, and the emission intensity per unit mileage at each speed stage is comprehensively processed in combination with the driving mileage to obtain the equivalent emission of each pollutant at each characteristic temperature point, and the relative emission factor at each characteristic temperature point is calculated. The emission sensitivity index of each pollutant is calculated based on the relative emission factor bias. Based on the emission sensitivity index, the segment boundary temperature of each pollutant is adaptively adjusted to determine the optimal segment temperature of each pollutant. The dynamic self-evolving segment boundary temperature is calculated based on the optimal segment temperature and used as the segment boundary. A first temperature-emission response model is constructed in the low-temperature region, and a second temperature-emission response model is constructed in the high-temperature region. The adaptive transition width parameters of each pollutant are calculated based on the emission sensitivity index, and a smooth transition weight function is constructed. The first temperature-emission response model and the second temperature-emission response model are then fused to obtain the predicted relative emission factors of each pollutant at any temperature. The predicted emissions of each pollutant at the target temperature are obtained by multiplying the predicted relative emission factor and the equivalent emissions at the baseline temperature.

2. The method for predicting multi-pollutant emissions from vehicles under WLTC conditions based on finite characteristic temperature points as described in claim 1, characterized in that, The calculation of the phase hot-state decay coefficient includes: in, For the first Characteristic temperature points Next Phase thermal decay coefficient at each velocity stage For the first Cold start thermal impact factors at each speed stage The normalized decay constant for hot recovery. This is the lowest characteristic temperature point. This is the point with the highest characteristic temperature.

3. The method for predicting multi-pollutant emissions from vehicles under WLTC conditions based on finite characteristic temperature points as described in claim 2, characterized in that... The equivalent emissions of each pollutant at each characteristic temperature point are obtained as follows: in, pollutants In the Characteristic temperature points The equivalent emissions below For the first Mileage at each speed level pollutants In the Characteristic temperature points Next Emission intensity per unit mileage at each speed stage pollutants In the Characteristic temperature points The residual thermal inertia memory factor; Calculate pollutants In the Characteristic temperature points Thermal inertia residual memory factor include: in, This is the thermal inertia amplification factor. In the first Characteristic temperature points Next The equivalent thermal energy storage density of the engine at the end of each speed phase For reference thermal energy storage density, This is the thermal decay time coefficient. Thermal inertia nonlinear index For the first The equivalent thermal interval time before the start of each velocity phase. This is the reference time constant.

4. The method for predicting multi-pollutant emissions from vehicles under WLTC conditions based on finite characteristic temperature points as described in claim 3, characterized in that... The calculation of the relative emission factor at each characteristic temperature point includes: in, pollutants In the Characteristic temperature points The relative emission factor under the following conditions pollutants At standard reference temperature The equivalent emissions below; The calculation of the emission sensitivity index for each pollutant includes: in, pollutants Emission sensitivity index As the first weight of the emissions sensitivity index, pollutants At the lowest characteristic temperature point The relative emission factor under the following conditions As the second weight of the emissions sensitivity index, pollutants Cold start characteristic temperature The relative emission factor.

5. The method for predicting multi-pollutant emissions from vehicles under WLTC conditions based on finite characteristic temperature points as described in claim 4, characterized in that... Determining the optimal segmented temperature for each pollutant includes: in, pollutants The optimal segmented temperature, This represents the maximum segmented temperature adjustment range. This is the arithmetic mean of the emission sensitivity indices for all pollutants.

6. The method for predicting multi-pollutant emissions from vehicles under WLTC conditions based on finite characteristic temperature points as described in claim 5, characterized in that... It also includes calculating the dynamic self-evolving segment boundary temperature based on the optimal segment temperature: in, For dynamically evolving segmented boundary temperatures, The curvature response modulation coefficient, The mean of the emission curvature entropy exponent for all pollutants. pollutants The emission curvature entropy index, This refers to the amplitude of dynamic boundary oscillations. Dynamically self-evolving segmented boundary temperature As segment boundaries.

7. The method for predicting multi-pollutant emissions from vehicles under WLTC conditions based on finite characteristic temperature points as described in claim 6, characterized in that... The first temperature-emission response model includes: in, Pollutants in the low-temperature zone At the target temperature The predicted relative emission factor is as follows. This is the leading factor for the low temperature index. The low-temperature index decay rate, This is the low-temperature reference bias term; The second temperature-emission response model includes: in, Pollutants in high-temperature zones At the target temperature The predicted relative emission factor is as follows. This is a secondary correction factor for high temperature. This is the primary correction factor for high temperature.

8. The method for predicting multi-pollutant emissions from vehicles under WLTC conditions based on finite characteristic temperature points as described in claim 7, characterized in that... The adaptive transition width parameters for each pollutant are calculated based on the emission sensitivity index, and a smooth transition weighting function is constructed, including: Calculate the adaptive transition width parameters for each pollutant: in, pollutants The adaptive transition width parameter, As the reference transition width parameter, For adaptive modulation coefficients; Construct a smooth transition weight function: in, Target temperature The value of the smooth transition weight function.

9. The method for predicting multi-pollutant emissions from vehicles under WLTC conditions based on finite characteristic temperature points as described in claim 8, characterized in that... The predicted relative emission factors for each pollutant at any temperature include: in, pollutants At the target temperature The predicted relative emission factor is as follows.

10. A vehicle WLTC multi-pollutant emission prediction system based on finite characteristic temperature points, characterized in that, include: The data acquisition module is used to complete the WLTC cycle test of the vehicle under test at a limited number of characteristic temperature points. At each characteristic temperature point, the emission data of each pollutant and the driving mileage are collected at each speed stage of the WLTC cycle to obtain the emission intensity per unit mileage at each speed stage. The module for calculating relative emission factors is used to construct the phase thermal decay coefficient of each speed stage at each characteristic temperature point, and to comprehensively process the emission intensity per unit mileage at each speed stage in combination with the driving mileage, so as to obtain the equivalent emission of each pollutant at each characteristic temperature point and calculate the relative emission factor at each characteristic temperature point. The module for determining the optimal segmented temperature is used to calculate the emission sensitivity index of each pollutant based on the relative emission factor bias, and to adaptively adjust the segmented boundary temperature of each pollutant based on the emission sensitivity index to determine the optimal segmented temperature of each pollutant. The model setting module is used to calculate the dynamic self-evolving segment boundary temperature based on the optimal segment temperature, and use it as the segment boundary to build a first temperature-emission response model in the low temperature region and a second temperature-emission response model in the high temperature region. The module for calculating the predicted relative emission factor is used to calculate the adaptive transition width parameter of each pollutant based on the emission sensitivity index, and to construct a smooth transition weight function to fuse the first temperature-emission response model and the second temperature-emission response model to obtain the predicted relative emission factor of each pollutant at any temperature. The prediction module is used to obtain the predicted emission values ​​of each pollutant at the target temperature by multiplying the predicted relative emission factor and the equivalent emission at the reference temperature.

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