An adaptive control method and system based on a DPF carbon loading model

Abstract

The application discloses a kind of λ self-adaptive control method and system based on DPF carbon load model, the method includes: the real-time mileage model value of vehicle, the real-time flow resistance model value of particle trap and the real-time original emission model value of engine are respectively obtained;Real-time flow resistance model value and real-time original emission model value are compared and maximum value is taken, and real-time DPF carbon load model value is output;According to real-time mileage model value and real-time DPF carbon load model value, real-time model deviation value is output;According to real-time model deviation value and the DPF maximum allowable carbon load value of particle trap, DPF carbon load model deviation coefficient value is output;Engine speed and DPF carbon load model deviation coefficient value are respectively used as the x, y axis of DPF carbon load model λ correction MAP, and z axis is λ correction coefficient;λ Correction coefficient of DPF carbon load model is input into smoke limit λ main strategy and is calculated and output.Through based on DPF carbon load model to smoke limit λ coefficient is adaptively corrected, can improve the transient power of vehicle in the process of vehicle use.

Description

Technical Field

[0001] This invention relates to the field of vehicle calibration technology, specifically to a λ-adaptive control method and system based on a DPF carbon load model. Background Technology

[0002] The smoke limit λ coefficient control for diesel vehicles can be set based on intake air volume and engine speed, and can also be corrected based on atmospheric pressure, intake air temperature and coolant temperature. The setting of the smoke limit λ coefficient directly affects the DPF carbon load rate and the vehicle's acceleration performance, and it is a tradeoff relationship. The DPF carbon load rate itself varies with the actual operating conditions of the vehicle. In order to ensure that the vehicle achieves the regeneration mileage target, the smoke limit λ coefficient setting usually prioritizes the regeneration mileage, which will sacrifice some power performance to a certain extent.

[0003] When a vehicle operates in a region with relatively good emissions from its original engine for an extended period, the carbon load rate of the DPF will be at a low level. Under appropriate scenarios and operating conditions, the smoke limit coefficient λ can be tilted towards power performance to slightly balance the carbon load rate.

[0004] Because DPF regeneration systems are prone to irreversible hardware failures, such as DPF erosion and blockage, diesel vehicles typically prioritize ensuring that the DPF regeneration mileage meets development targets and leave margins for variance in their smoke limit coefficient calibration strategies. This results in a relatively small smoke limit coefficient during acceleration, and insufficient transient intake volume leads to a reduction in fuel injection, thus sacrificing acceleration performance to some extent. Furthermore, due to the robustness requirements of the vehicle's SCR module and DPF regeneration safety, a maximum interval mileage must be set for the DPF regeneration system. This means that regardless of whether the DPF carbon load is full, DPF regeneration will be triggered directly when the vehicle reaches the maximum set mileage. Based on these two reasons, and the analysis of actual user big data, it has been found that most users' operating conditions involve triggering the DPF regeneration mileage by setting the maximum interval mileage before the DPF is fully charged with carbon smoke. Therefore, the existing technology still suffers from the problem that pre-calibrating the smoke limit coefficient reduces vehicle performance and cannot quickly improve it. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention aims to provide a λ-adaptive control method and system based on the DPF carbon load model, which solves the problem that the pre-calibration of the smoke limitation λ coefficient in existing technologies reduces vehicle dynamics and cannot be rapidly improved.

[0006] A first aspect of the present invention is to provide a λ-adaptive control method based on a DPF carbon load model, the method comprising: The real-time mileage model value of the vehicle, the real-time flow resistance model value of the particulate filter, and the real-time exhaust model value of the engine are obtained respectively. The real-time flow resistance model value is compared with the real-time original discharge model value, and the maximum value is taken to output the real-time DPF carbon load model value; Based on the real-time mileage model value and the real-time DPF carbon load model value, output the real-time model deviation value; Based on the real-time model deviation value and the maximum allowable carbon load value of the particulate filter's DPF, output the DPF carbon load model deviation coefficient value; The engine speed and the deviation coefficient value of the DPF carbon load model are respectively used as the x and y axes of the DPF carbon load model λ correction MAP, and the z axis is the λ correction coefficient. The λ correction coefficient of the DPF carbon load model is input into the smoke limitation λ main strategy for calculation and output.

[0007] According to one aspect of the above technical solution, the step of obtaining the real-time mileage model value of the vehicle includes: The vehicle's speed and duration during the current driving phase are obtained, and a real-time mileage model value is calculated based on the speed and duration.

[0008] According to one aspect of the above technical solution, the step of obtaining the real-time flow resistance model value of the particle trap includes: The pressure difference between the DPF input and output terminals of the particulate filter in the vehicle and the exhaust flow rate at the output terminal are obtained, and the real-time flow resistance model value is calculated based on the pressure difference between the DPF and the exhaust flow rate.

[0009] According to one aspect of the above technical solution, the step of obtaining the real-time original exhaust model value of the engine includes: Obtain the static and transient emissions of the engine in the vehicle, and calculate the real-time original emission model value based on the static emissions, transient emissions, and passive regeneration model.

[0010] According to one aspect of the above technical solution, the step of outputting a real-time model deviation value based on the real-time mileage model value and the real-time DPF carbon load model value includes: The real-time model deviation value is obtained by subtracting the real-time DPF carbon load model value from the real-time mileage model value.

[0011] According to one aspect of the above technical solution, the step of outputting the DPF carbon load model deviation coefficient value based on the real-time model deviation value and the maximum allowable carbon load value of the particulate filter's DPF includes: Divide the real-time model deviation value by the maximum allowable carbon load of the DPF to obtain the DPF carbon load model deviation coefficient value.

[0012] According to one aspect of the above technical solution, the λ correction coefficient can be positive or negative, representing either stricter or more relaxed smoke opacity restrictions, respectively.

[0013] A second aspect of the present invention is to provide a λ-adaptive control system based on a DPF carbon load model, applied to the method described in the above-mentioned technical solution, the system comprising: The first processing module is used to obtain the real-time mileage model value of the vehicle, the real-time flow resistance model value of the particulate filter, and the real-time exhaust model value of the engine, respectively. The second processing module is used to compare the real-time flow resistance model value with the real-time original exhaust model value and take the maximum value, and output the real-time DPF carbon load model value. The third processing module is used to output a real-time model deviation value based on the real-time mileage model value and the real-time DPF carbon load model value. The fourth processing module is used to output the DPF carbon load model deviation coefficient value based on the real-time model deviation value and the maximum allowable carbon load value of the particulate filter's DPF. The coefficient correction module is used to take the deviation coefficient value between the engine speed and the DPF carbon load model as the x and y axes of the DPF carbon load model λ correction MAP, respectively, and the z axis as the λ correction coefficient. The strategy adjustment module is used to input the λ correction coefficient of the DPF carbon load model into the smoke limit λ main strategy for calculation and output.

[0014] A third aspect of the present invention is to provide a readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the steps of the method described in the above-described technical solution.

[0015] A fourth aspect of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method described in the above technical solutions.

[0016] Compared with existing technologies, the λ-adaptive control method and system based on the DPF carbon load model shown in this invention have the following advantages: The method described in this invention acquires the real-time mileage model value of the vehicle, the real-time flow resistance model value of the particulate filter, and the real-time exhaust model value of the engine. It compares the real-time flow resistance model value with the real-time exhaust model value and takes the maximum value to output the real-time DPF carbon load model value. Based on the real-time mileage model value and the real-time DPF carbon load model value, it outputs the real-time model deviation value. Based on the real-time model deviation value and the maximum permissible carbon load value of the particulate filter's DPF, it outputs the DPF carbon load model deviation coefficient value. The engine speed and the DPF carbon load model deviation coefficient value are used as the x and y axes of the DPF carbon load model λ correction MAP, respectively, with the z-axis representing the λ correction coefficient. The λ correction coefficient of the DPF carbon load model is input into the smoke limitation λ main strategy for calculation and output. By adaptively correcting the smoke limitation λ coefficient based on the DPF carbon load model, the method described in this invention can improve the transient dynamic performance of the vehicle during use, and vehicle calibration is more convenient. It also makes it easier to achieve an effective balance between the margin of the DPF regeneration system and the dynamic performance. Attached Figure Description

[0017] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a flowchart illustrating the λ adaptive control method based on the DPF carbon load model in one embodiment of the present invention. Figure 2 This is a structural block diagram of a λ adaptive control system based on a DPF carbon load model in one embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Several embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of the present invention will be more thorough and complete.

[0019] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0021] In related technologies, the smoke limitation λ coefficient correction is mainly based on conditions such as atmospheric pressure, intake air temperature, and cooling water temperature, and is mainly corrected in the direction of limiting power performance. The purpose is to reduce carbon emissions during the transient process of diesel engines, which reduces the user experience for some users.

[0022] Example 1 Please see Figure 1 The first embodiment of the present invention provides a λ adaptive control method based on a DPF carbon load model, the method comprising steps S10-S60: Step S10: Obtain the real-time mileage model value of the vehicle, the real-time flow resistance model value of the particulate filter, and the real-time exhaust model value of the engine.

[0023] In this embodiment, the step of obtaining the real-time mileage model value of the vehicle includes: The vehicle's speed and duration during the current driving phase are obtained, and a real-time mileage model value is calculated based on the speed and duration.

[0024] Specifically, when obtaining the real-time mileage model value of a vehicle, the vehicle's driving speed and driving time in the current driving phase are first obtained. The driving speed can be the actual real-time speed or the average real-time speed in the current driving phase. Then, the real-time mileage model value is calculated based on the driving speed and driving time.

[0025] In this embodiment, the step of obtaining the real-time flow resistance model value of the particle trap includes: The pressure difference between the DPF input and output terminals of the particulate filter in the vehicle and the exhaust flow rate at the output terminal are obtained, and the real-time flow resistance model value is calculated based on the pressure difference between the DPF and the exhaust flow rate.

[0026] Specifically, when obtaining the real-time flow resistance model value of the particulate filter (DPF) in the vehicle, the pressure difference between the DPF input and output ends of the particulate filter is first obtained. Specifically, the output DPF pressure difference is calculated based on the actual pressure at the input and output ends, and the exhaust flow rate at the output end is obtained. Based on the aforementioned DPF pressure difference and exhaust flow rate, the real-time flow resistance model value corresponding to the particulate filter (DPF) is calculated.

[0027] In this embodiment, the step of obtaining the real-time original exhaust model value of the engine includes: Obtain the static and transient emissions of the engine in the vehicle, and calculate the real-time original emission model value based on the static emissions, transient emissions, and passive regeneration model.

[0028] Specifically, when obtaining the real-time original exhaust model value of the engine in a car, the static emission and transient emission of the engine are first obtained. The static emission is the original static emission of the engine, and the transient emission is the instantaneous emission corresponding to the current throttle opening. Then, the real-time original exhaust model value is calculated and output based on the engine's static emission, transient emission and passive regeneration model.

[0029] Step S20: Compare the real-time flow resistance model value with the real-time original exhaust model value and take the maximum value to output the real-time DPF carbon load model value.

[0030] Specifically, in this embodiment, the step of outputting the real-time model deviation value based on the real-time odometer model value and the real-time DPF carbon load model value includes: The real-time model deviation value is obtained by subtracting the real-time DPF carbon load model value from the real-time mileage model value.

[0031] Step S30: Output the real-time model deviation value based on the real-time mileage model value and the real-time DPF carbon load model value.

[0032] Step S40: Output the DPF carbon load model deviation coefficient value based on the real-time model deviation value and the maximum allowable carbon load value of the particulate filter's DPF.

[0033] It should be noted that the maximum allowable carbon load of the DPF is a constant value determined by the hardware characteristics of the particulate filter.

[0034] Specifically, in this embodiment, the step of outputting the DPF carbon load model deviation coefficient value based on the real-time model deviation value and the maximum allowable carbon load value of the particulate filter's DPF includes: Divide the real-time model deviation value by the maximum allowable carbon load of the DPF to obtain the DPF carbon load model deviation coefficient value.

[0035] Step S50: The engine speed and the deviation coefficient value of the DPF carbon load model are respectively used as the x and y axes of the DPF carbon load model λ correction MAP, and the z axis is the λ correction coefficient.

[0036] The engine speed is used as the x-axis, with the unit being rpm. The λ correction coefficient is either positive or negative, representing stricter or more lenient smoke opacity restrictions, respectively.

[0037] Step S60: Input the λ correction coefficient of the DPF carbon load model into the smoke limitation λ main strategy for calculation and output.

[0038] Table 1 shows a comparison of data for λ correction performed by the method described in this embodiment.

[0039] Table 1

[0040] In existing technologies, to ensure the robustness of the DPF regeneration system for all user vehicles, a significant amount of power is sacrificed. For example, for users with relatively good driving conditions (such as long-distance high-speed driving) and good driving habits (such as fewer rapid acceleration and deceleration), the engine carbon emissions of these users' vehicles are actually relatively low, the DPF carbon load rate is very low, and carbon soot is almost not fully accumulated, triggering regeneration only at the maximum mileage. However, these users often require higher power output, such as high-speed overtaking and climbing long slopes. This embodiment differentiates the processing by using a DPF carbon load model correction strategy to release the power that the vehicle can release as steadily as possible without affecting the reliability of the DPF regeneration system.

[0041] Compared with existing technologies, the λ-adaptive control method based on the DPF carbon load model shown in this embodiment has the following advantages: The method described in this embodiment obtains the real-time mileage model value of the vehicle, the real-time flow resistance model value of the particulate filter, and the real-time original exhaust model value of the engine, respectively. It compares the real-time flow resistance model value with the real-time original exhaust model value and takes the maximum value to output the real-time DPF carbon load model value. Based on the real-time mileage model value and the real-time DPF carbon load model value, it outputs the real-time model deviation value. Based on the real-time model deviation value and the maximum allowable carbon load value of the particulate filter's DPF, it outputs the DPF carbon load model deviation coefficient value. The engine speed and the DPF carbon load model deviation coefficient value are used as the x and y axes of the DPF carbon load model λ correction MAP, respectively, with the z-axis representing the λ correction coefficient. The λ correction coefficient of the DPF carbon load model is input into the smoke limitation λ main strategy for calculation and output. By adaptively correcting the smoke limitation λ coefficient based on the DPF carbon load model, the method described in this embodiment can improve the vehicle's transient dynamic performance during vehicle use, and vehicle calibration is more convenient. It also makes it easier to achieve an effective balance between the DPF regeneration system margin and dynamic performance.

[0042] Example 2 Please see Figure 2 The second embodiment of the present invention provides a λ-adaptive control system based on a DPF carbon load model, applied to the method described in the above embodiments, wherein the system includes: The first processing module 10 is used to acquire the real-time mileage model value of the vehicle, the real-time flow resistance model value of the particulate filter, and the real-time exhaust model value of the engine, respectively. The second processing module 20 is used to compare the real-time flow resistance model value with the real-time original exhaust model value and take the maximum value, and output the real-time DPF carbon load model value. The third processing module 30 is used to output a real-time model deviation value based on the real-time mileage model value and the real-time DPF carbon load model value. The fourth processing module 40 is used to output the DPF carbon load model deviation coefficient value based on the real-time model deviation value and the maximum allowable carbon load value of the particulate filter's DPF. The coefficient correction module 50 is used to take the engine speed and the deviation coefficient value of the DPF carbon load model as the x and y axes of the DPF carbon load model λ correction MAP, respectively, and the z axis as the λ correction coefficient. The strategy adjustment module 60 is used to input the λ correction coefficient of the DPF carbon load model into the smoke limit λ main strategy for calculation and output.

[0043] Compared with existing technologies, the λ-adaptive control system based on the DPF carbon load model shown in this embodiment has the following advantages: The system shown in this embodiment acquires the real-time mileage model value of the vehicle, the real-time flow resistance model value of the particulate filter, and the real-time original exhaust model value of the engine. It compares the real-time flow resistance model value with the real-time original exhaust model value and takes the maximum value to output the real-time DPF carbon load model value. Based on the real-time mileage model value and the real-time DPF carbon load model value, it outputs the real-time model deviation value. Based on the real-time model deviation value and the maximum permissible carbon load value of the particulate filter's DPF, it outputs the DPF carbon load model deviation coefficient value. The engine speed and the DPF carbon load model deviation coefficient value are used as the x and y axes of the DPF carbon load model λ correction MAP, respectively, with the z-axis representing the λ correction coefficient. The λ correction coefficient of the DPF carbon load model is input into the smoke limitation λ main strategy for calculation and output. By adaptively correcting the smoke limitation λ coefficient based on the DPF carbon load model, the system shown in this embodiment can improve the vehicle's transient dynamic performance during vehicle use, and vehicle calibration is more convenient. It also makes it easier to achieve an effective balance between the DPF regeneration system margin and dynamic performance.

[0044] Example 3 A third embodiment of the present invention provides a readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the steps of the method described in any of the above embodiments.

[0045] Example 4 A fourth embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method described in any of the above embodiments.

[0046] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0047] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. An adaptive control method based on a DPF carbon load model, characterized in that, The method includes: The real-time mileage model value of the vehicle, the real-time flow resistance model value of the particulate filter, and the real-time exhaust model value of the engine are obtained respectively. The real-time flow resistance model value is compared with the real-time original discharge model value, and the maximum value is taken to output the real-time DPF carbon load model value; Based on the real-time mileage model value and the real-time DPF carbon load model value, output the real-time model deviation value; Based on the real-time model deviation value and the maximum allowable carbon load value of the particulate filter's DPF, output the DPF carbon load model deviation coefficient value; The engine speed and the deviation coefficient of the DPF carbon load model are respectively used as the x and y axes of the DPF carbon load model correction MAP, and the z axis is the correction coefficient. The correction coefficients of the DPF carbon load model are input into the smoke limitation main strategy for calculation and output; The steps for obtaining the real-time original exhaust model values ​​of the engine include: Obtain the static and transient emissions of the engine in the vehicle, and calculate the real-time original emission model value based on the static emissions, transient emissions, and passive regeneration model; The step of outputting the real-time model deviation value based on the real-time mileage model value and the real-time DPF carbon load model value includes: Subtract the real-time DPF carbon load model value from the real-time mileage model value to obtain the real-time model deviation value; The step of outputting the DPF carbon load model deviation coefficient value based on the real-time model deviation value and the maximum allowable carbon load of the particulate filter includes: Divide the real-time model deviation value by the maximum allowable carbon load of DPF to obtain the DPF carbon load model deviation coefficient value; The correction factor can be positive or negative, representing either stricter or more relaxed smoke opacity restrictions, respectively.

2. The adaptive control method for the DPF carbon carrier model according to claim 1, characterized in that, The steps to obtain the real-time mileage model value of a vehicle include: The vehicle's speed and duration during the current driving phase are obtained, and a real-time mileage model value is calculated based on the speed and duration.

3. The adaptive control method for the DPF carbon carrier model according to claim 1, characterized in that, The steps for obtaining the real-time flow resistance model value of the particle trap include: The pressure difference between the DPF input and output terminals of the particulate filter in the vehicle and the exhaust flow rate at the output terminal are obtained, and the real-time flow resistance model value is calculated based on the pressure difference between the DPF and the exhaust flow rate.

4. An adaptive control system based on a DPF carbon load model, characterized in that, The system, applicable to the method of any one of claims 1-3, comprises: The first processing module is used to obtain the real-time mileage model value of the vehicle, the real-time flow resistance model value of the particulate filter, and the real-time exhaust model value of the engine, respectively. The second processing module is used to compare the real-time flow resistance model value with the real-time original exhaust model value and take the maximum value, and output the real-time DPF carbon load model value. The third processing module is used to output a real-time model deviation value based on the real-time mileage model value and the real-time DPF carbon load model value. The fourth processing module is used to output the DPF carbon load model deviation coefficient value based on the real-time model deviation value and the maximum allowable carbon load value of the particulate filter's DPF. The coefficient correction module is used to take the engine speed and the deviation coefficient value of the DPF carbon load model as the x and y axes of the DPF carbon load model correction MAP, respectively, and the z axis as the correction coefficient. The strategy adjustment module is used to input the correction coefficients of the DPF carbon load model into the smoke limitation main strategy for calculation and output.

5. A readable storage medium having computer instructions stored thereon, characterized in that, When executed by the processor, this instruction implements the steps of the method as described in any one of claims 1-3.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 1-3.

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