A DPF driving regeneration control method and device and a storage medium

Abstract

The application discloses a DPF driving regeneration control method, device and storage medium, which is used for starting DPF driving regeneration in time and preventing DPF from being burnt through. The DPF driving regeneration control method comprises the following steps: according to different engine and automobile models, a risk model under multiple scenes is established in advance; real-time data and scene information are acquired; a risk model under a corresponding scene is searched according to the scene information; the real-time data are input into the risk model under the corresponding scene, a target risk coefficient and a correction coefficient are calculated, and the target risk coefficient and the correction coefficient correspond to each other; a DPF regeneration risk coefficient is calculated according to the target risk coefficient and the correction coefficient; the DPF regeneration risk coefficient is graded to obtain a grading result; and the automobile and the engine mode are controlled according to the grading result.

Description

Technical Field

[0001] This application relates to the field of engine aftertreatment system technology, and in particular to a DPF driving regeneration control method, device and storage medium. Background Technology

[0002] A diesel particulate filter (DPF) is a common emission control device in automobiles. It captures tiny particles in emissions, thereby reducing vehicle pollutant emissions. When a diesel engine is running, the exhaust gases produced by combustion pass through the DPF filter. Tiny particles are captured in the filter, while larger gas molecules pass through and are emitted. When particulate matter accumulates in the DPF to a certain level, it can cause poor exhaust flow, affecting engine power and fuel consumption, requiring timely cleaning. Cleaning the DPF of accumulated carbon is called DPF regeneration, and DPF regeneration during vehicle operation is called DPF on-road regeneration.

[0003] Currently, the DPF regeneration process involves the engine entering a regeneration mode after the DPF carbon load reaches a certain limit. This increases exhaust temperature and releases far-injector fuel to raise the DPF temperature to around 600°C, allowing the accumulated carbon in the DPF to be quickly removed by reacting with oxygen. However, this current method prioritizes rapid regeneration without considering the vehicle, engine, aftertreatment system, or environmental conditions, potentially leading to engine failure and DPF burn-out. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a DPF (Device Filter) travel regeneration control method, apparatus, and storage medium for timely initiation of DPF travel regeneration to prevent DPF burn-out. The technical solution provided in this application is described below:

[0005] The first aspect of this application provides a DPF (Driving Power Filter) vehicle regeneration control method, comprising:

[0006] Risk models are pre-established for various scenarios based on different engine and car models;

[0007] Acquire real-time data and scenario information, including: vehicle data, engine data, DPF data, diesel oxidation catalyst (DOC) data, and environmental data;

[0008] Find the risk model for the corresponding scenario based on the scenario information;

[0009] The real-time data is input into the risk model under the corresponding scenario to calculate the target risk coefficient and the correction coefficient, wherein the target risk coefficient and the correction coefficient are in one-to-one correspondence.

[0010] The DPF regeneration risk coefficient is calculated based on the target risk coefficient and the correction coefficient.

[0011] The DPF regeneration risk coefficient is used to assign a rating, and the rating result is obtained.

[0012] The vehicle and engine modes are controlled based on the rating results.

[0013] Optionally, the risk models pre-established for various scenarios based on different engine and vehicle models include:

[0014] Obtain different engine and car models;

[0015] By combining the engine model and the car model, various combinations of engines and cars can be obtained;

[0016] Analyze the risk levels of various engine and vehicle combinations in multiple practical scenarios;

[0017] A risk model is established based on the risk level, and the risk model includes: vehicle speed range model, throttle range model, battery voltage range model, engine speed range model, circulating oil volume range model, coolant temperature range model, intercooler after-temperature range model, DOC before temperature range model, DPF before temperature range model, ambient temperature range model, and atmospheric pressure range model.

[0018] Optionally, the target risk coefficients include: vehicle speed risk coefficient, throttle risk coefficient, battery voltage risk coefficient, engine speed risk coefficient, circulating oil volume risk coefficient, coolant temperature risk coefficient, intercooler after-temperature risk coefficient, DOC inlet temperature risk coefficient, DPF inlet temperature risk coefficient, ambient temperature risk coefficient, and atmospheric pressure risk coefficient.

[0019] Optionally, the step of calculating the DPF regeneration risk coefficient based on the target risk coefficient and the correction coefficient includes:

[0020] Obtain the calculation formula, which is:

[0021] η=A×α+B×β+C×γ+D×δ+E×ε+F×ρ+G×θ+H×σ+

[0022] I×μ+J×ρ+K×φ;

[0023] Where η represents the DPF regeneration risk coefficient, A represents the vehicle speed risk coefficient, B represents the throttle risk coefficient, C represents the battery voltage risk coefficient, D represents the engine speed risk coefficient, E represents the circulating oil quantity risk coefficient, F represents the coolant temperature risk coefficient, G represents the intercooler after-temperature risk coefficient, H represents the DOC inlet temperature risk coefficient, I represents the DPF inlet temperature risk coefficient, J represents the ambient temperature risk coefficient, and K represents the atmospheric pressure risk coefficient; α represents the correction coefficient corresponding to A, β represents the correction coefficient corresponding to B, γ represents the correction coefficient corresponding to C, δ represents the correction coefficient corresponding to D, ε represents the correction coefficient corresponding to E, ρ represents the correction coefficient corresponding to F, θ represents the correction coefficient corresponding to G, σ represents the correction coefficient corresponding to H, μ represents the correction coefficient corresponding to I, ρ represents the correction coefficient corresponding to J, and φ represents the correction coefficient corresponding to K.

[0024] The DPF regeneration risk coefficient is calculated according to the formula.

[0025] Optionally, controlling the vehicle and engine modes based on the rating results includes:

[0026] When the rating result is high risk, the engine is kept in normal mode so that the vehicle exits DPF vehicle regeneration or is prohibited from entering DPF vehicle regeneration.

[0027] Optionally, controlling the vehicle and engine modes based on the rating results includes:

[0028] When the rating result is medium risk, the engine warm-up mode is activated, and the vehicle is prohibited from entering DPF driving regeneration.

[0029] Optionally, controlling the vehicle and engine modes based on the rating results includes:

[0030] When the rating result is low risk, the engine's DOC heating mode is activated to cause the vehicle to exit DPF on-board regeneration or prevent the vehicle from entering DPF on-board regeneration.

[0031] Optionally, controlling the vehicle and engine modes based on the rating results includes:

[0032] When the rating result is no risk, the engine is kept in driving regeneration mode, allowing the vehicle to enter or continue DPF driving regeneration.

[0033] A second aspect of this application provides a DPF (Driving Power Filter) vehicle regeneration control device, comprising:

[0034] A risk model building unit is used to pre-build risk models in various scenarios based on different engine and vehicle models.

[0035] The acquisition unit is used to acquire real-time data and scene information, wherein the real-time data includes: vehicle data, engine data, DPF data, DOC data and environmental data;

[0036] A search unit, configured to search for a risk model for a corresponding scenario based on the scenario information;

[0037] The first calculation unit is used to input the real-time data into the risk model under the corresponding scenario, and calculate the target risk coefficient and the correction coefficient, wherein the target risk coefficient and the correction coefficient are in one-to-one correspondence.

[0038] The second calculation unit is used to calculate the DPF regeneration risk coefficient based on the target risk coefficient and the correction coefficient.

[0039] A rating unit is used to rate the regeneration risk coefficient of the DPF and obtain a rating result.

[0040] A control unit for controlling the vehicle and engine modes based on the rating results.

[0041] A third aspect of this application provides a DPF (Driving Power Filter) regeneration control device, the device comprising:

[0042] Processor, memory, input / output units, and bus;

[0043] The processor is connected to the memory, the input / output unit, and the bus;

[0044] The memory stores a program, which the processor invokes to execute the first aspect and any optional aspect of the DPF vehicle regeneration control method.

[0045] The fourth aspect of this application provides a computer-readable storage medium storing a program that, when executed on a computer, performs the DPF vehicle regeneration control method of the first aspect and any one of the first aspects.

[0046] As can be seen from the above technical solutions, this application has the following advantages:

[0047] The DPF regeneration control method of this application pre-establishes a risk model, then inputs real-time data into the risk model to obtain a target risk coefficient and a correction coefficient, and then calculates the DPF regeneration risk coefficient based on the target risk coefficient and the correction coefficient. This DPF regeneration risk coefficient fully considers various influencing factors in the real-time data and comprehensively analyzes the safety of DPF regeneration. Furthermore, a risk level rating is performed based on the DPF regeneration risk coefficient, and the engine mode is determined by the risk rating, ensuring both the safety of the engine and the DPF, while also meeting the requirements of DPF regeneration. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a schematic flowchart of a DPF (Driving Power Filter) vehicle regeneration control method provided in one embodiment of this application;

[0050] Figure 2 This is a schematic flowchart of a DPF vehicle regeneration control method provided in another embodiment of this application;

[0051] Figure 3 This is a schematic diagram of the structure of a DPF (Device Processing Filter) vehicle regeneration control device provided in one embodiment of this application;

[0052] Figure 4 A schematic diagram of the structure of a DPF vehicle regeneration control device provided in another embodiment of this application;

[0053] Figure 5 This is a schematic diagram of the structure of a DPF (Driving Power Filter) vehicle regeneration control device provided in another embodiment of this application. Detailed Implementation

[0054] It should be noted that the DPF (Device Filter) vehicle regeneration control method provided in this application can be applied to terminals, systems, and servers. For example, the terminal can be a smartphone, computer, tablet, smart TV, smartwatch, portable computer, or a desktop computer, etc. For ease of explanation, this application uses the terminal as the implementing entity for illustration.

[0055] Please see Figure 1 , Figure 1 A schematic flowchart of an embodiment of the DPF (Driving Power Filter) vehicle regeneration control method provided in this application is shown. The method includes:

[0056] S101. Based on different engine and vehicle models, risk models are pre-established for various scenarios;

[0057] In this embodiment, vehicle particulate matter emissions must meet the requirements clearly stipulated by national standards. Since particulate matter emissions are closely related to the engine and vehicle model, the prerequisite for safely and accurately controlling DPF regeneration during operation is to consider multiple factors, including the vehicle, engine, aftertreatment status, and environment. Therefore, during the diesel engine development stage, based on the matching of engine and vehicle models and the conditions of various usage scenarios, combined with the risk level of each scenario, multiple risk models are pre-established. These models allow for subsequent data input for calculations, accurately determining whether to activate DPF regeneration during operation.

[0058] S102. Obtain real-time data and scene information, wherein the real-time data includes: vehicle data, engine data, DPF data, DOC data and environmental data;

[0059] In this embodiment, whether the vehicle performs DPF (Driving Power Filter) regeneration is affected by the current state of the vehicle and the current driving environment, thus requiring the acquisition of real-time data and scenario information. Real-time data is related to the current state of the vehicle and includes vehicle data, engine data, DPF data, DOC (Dual Activity Controller) data, and environmental data. Scenario information is used in subsequent steps to find the corresponding risk model. Vehicle data includes vehicle speed, throttle position, and battery voltage; engine data includes engine speed, circulating oil volume, coolant temperature, and intercooler afterheating temperature; DPF data includes the temperature before the DPF; DOC data includes the temperature before the DOC; and environmental data includes ambient temperature and atmospheric pressure.

[0060] S103. Find the risk model for the corresponding scenario based on the scenario information;

[0061] In this embodiment, the scenario information obtained in step S102 is used to determine the current vehicle usage scenario, and then to search for the corresponding risk model among the pre-established multiple risk models, so as to more accurately analyze the DPF driving regeneration risk in the current scenario.

[0062] S104. Input the real-time data into the risk model under the corresponding scenario, and calculate the target risk coefficient and the correction coefficient, wherein the target risk coefficient and the correction coefficient are in one-to-one correspondence.

[0063] In this embodiment, the real-time data obtained in step S102 is input into the risk model to quantify the risk level into more intuitive data, that is, to calculate multiple target risk coefficients and correction coefficients. Each target risk coefficient has a corresponding correction coefficient, and the correspondence is detailed in step S105.

[0064] S105. Calculate the DPF regeneration risk coefficient based on the target risk coefficient and the correction coefficient;

[0065] In this embodiment, to comprehensively assess the DPF regeneration risk based on multiple data points, the DPF regeneration risk coefficient must be calculated using the following formula:

[0066] η=A×α+B×β+C×γ+D×δ+E×ε+F×ρ+G×θ+H×σ+I×μ+J×ρ+K×φ;

[0067] Where η represents the DPF regeneration risk coefficient, A represents the vehicle speed risk coefficient, B represents the throttle risk coefficient, C represents the battery voltage risk coefficient, D represents the engine speed risk coefficient, E represents the circulating oil quantity risk coefficient, F represents the coolant temperature risk coefficient, G represents the intercooler after-temperature risk coefficient, H represents the DOC inlet temperature risk coefficient, I represents the DPF inlet temperature risk coefficient, J represents the ambient temperature risk coefficient, and K represents the atmospheric pressure risk coefficient; α represents the correction coefficient corresponding to A, β represents the correction coefficient corresponding to B, γ represents the correction coefficient corresponding to C, δ represents the correction coefficient corresponding to D, ε represents the correction coefficient corresponding to E, ρ represents the correction coefficient corresponding to F, θ represents the correction coefficient corresponding to G, σ represents the correction coefficient corresponding to H, μ represents the correction coefficient corresponding to I, ρ represents the correction coefficient corresponding to J, and φ represents the correction coefficient corresponding to K.

[0068] S106. Rating based on the DPF regeneration risk coefficient and obtaining the rating result;

[0069] In this embodiment, after obtaining the DPF regeneration risk coefficient η in step S105, the rating is queried in the pre-established database to obtain the rating result, which is one of four results: high risk, medium risk, low risk, and no risk.

[0070] S107. Control the vehicle and engine modes according to the rating results.

[0071] In this embodiment, the rating results can intuitively determine the level of risk. Based on the rating results, the vehicle and engine can be controlled, and DPF regeneration can be performed within a controllable risk range to protect the engine and DPF.

[0072] The DPF regeneration control method of this application pre-establishes a risk model, then inputs real-time data into the risk model to obtain a target risk coefficient and a correction coefficient, and then calculates the DPF regeneration risk coefficient based on the target risk coefficient and the correction coefficient. This DPF regeneration risk coefficient fully considers various influencing factors in the real-time data and comprehensively analyzes the safety of DPF regeneration. Furthermore, a risk level rating is performed based on the DPF regeneration risk coefficient, and the engine mode is determined by the risk rating, ensuring both the safety of the engine and the DPF, while also meeting the requirements of DPF regeneration.

[0073] In practice, pre-establishing risk models under multiple scenarios is an important prerequisite for realizing the DPF vehicle regeneration control method of this application. The following is another embodiment to illustrate in detail the pre-established risk models under multiple scenarios:

[0074] See Figure 2 , Figure 2 This is a schematic flowchart of a DPF (Driving Power Filter) vehicle regeneration control method according to another embodiment of this application. The method includes:

[0075] S201. Obtain different engine and vehicle models;

[0076] In this embodiment, the particulate matter emitted varies depending on the model of the engine and the vehicle, therefore it is necessary to collect data on different engine and vehicle models.

[0077] S202. Combine the engine model and the vehicle model to obtain multiple combinations of engines and vehicles;

[0078] In this embodiment, there are many different engine and car models in reality, and there are also many combinations of engines and cars. In order to achieve accurate control of DPF vehicle regeneration, it is necessary to analyze the various combinations.

[0079] S203. Analyze the risk level of various combinations of engines and vehicles in various practical scenarios;

[0080] In this embodiment, after obtaining the combination data of various engines and vehicles in step S202, each combination is further applied to different usage scenarios to obtain usage data of each combination in each usage scenario, and the risk level of each combination in each usage scenario is obtained based on the usage data analysis.

[0081] S204. Establish a risk model based on the stated risk level;

[0082] In this embodiment, risk models are specifically established according to the degree of risk, including vehicle speed range model, throttle range model, battery voltage range model, engine speed range model, circulating oil volume range model, coolant temperature range model, intercooler after-temperature range model, DOC before temperature range model, DPF before temperature range model, ambient temperature range model, and atmospheric pressure range model.

[0083] S205. Obtain real-time data and scene information;

[0084] Step S205 in this embodiment is the same as described above. Figure 1 Step S102 in the illustrated embodiment is similar and will not be described again here.

[0085] S206. Find the risk model for the corresponding scenario based on the scenario information;

[0086] Step S206 in this embodiment is the same as described above. Figure 1 Step S103 in the illustrated embodiment is similar and will not be repeated here.

[0087] S207. Input the real-time data into the risk model under the corresponding scenario, and calculate the target risk coefficient and the correction coefficient, wherein the target risk coefficient and the correction coefficient are in one-to-one correspondence.

[0088] In this embodiment, the risk model in step S204 is used to calculate the corresponding risk coefficient and its correction coefficient. For example, when engine A and car B are used together in scenario M, the vehicle speed in the real-time data is input into the corresponding vehicle speed range model to calculate the vehicle speed risk coefficient and its corresponding correction coefficient. The processing method for other data in the real-time data is similar to that for vehicle speed, and will not be described in detail here.

[0089] S208. Calculate the DPF regeneration risk coefficient based on the target risk coefficient and the correction coefficient;

[0090] Step S208 in this embodiment is the same as described above. Figure 1 Step S105 in the illustrated embodiment is similar and will not be repeated here.

[0091] S209. Rating based on the DPF regeneration risk coefficient and obtaining the rating result;

[0092] Step S209 in this embodiment is the same as described above. Figure 1 Step S106 in the illustrated embodiment is similar and will not be repeated here.

[0093] S210. Control the vehicle and engine modes according to the rating results.

[0094] Optionally, when the rating result is high risk, it indicates that the engine or aftertreatment is not in normal condition. The engine is kept in normal mode so that the vehicle exits DPF on-road regeneration or the vehicle is prohibited from entering DPF on-road regeneration.

[0095] Optionally, when the rating result is medium risk, it indicates that the car has just been started and is not fully warmed up. In this case, the engine warm-up mode is activated, and the car is prohibited from entering DPF driving regeneration mode.

[0096] Optionally, when the rating is low risk, it indicates that the aftertreatment temperature is either very high or very low, making it unsuitable to release the remote after-injection. If the temperature is too high, releasing the remote after-injection will result in an even higher exhaust temperature, burning through the DPF; if the temperature is too low, releasing the remote after-injection will prevent the injected diesel fuel from being converted, resulting in white smoke and an unpleasant odor when it is directly discharged. In this case, the engine's DOC heating mode is activated to either disable the vehicle from DPF on-road regeneration or prevent the vehicle from entering DPF on-road regeneration.

[0097] Optionally, when the rating result is risk-free, it means that the vehicle, engine, DOC, DPF and environment all meet the conditions for vehicle regeneration, the engine is kept in vehicle regeneration mode, and the vehicle enters or continues DPF vehicle regeneration.

[0098] This embodiment analyzes and judges the DPF on-road regeneration status based on the specific engine and vehicle model and usage scenario, so that the engine and vehicle can control DPF on-road regeneration in four modes respectively. Each operating mode has been specifically differentiated according to the corresponding risk level, thereby improving the accuracy and safety of DPF on-road regeneration.

[0099] The above embodiments describe the DPF (Device Filter) vehicle regeneration control method provided in this application. The following describes embodiments of the DPF vehicle regeneration control device and storage medium provided in this application:

[0100] See Figure 3 , Figure 3 This is a schematic diagram of the structure of a DPF (Device Power Filter) vehicle regeneration control device according to an embodiment of this application. The DPF vehicle regeneration control device includes:

[0101] Establishment unit 301 is used to pre-establish risk models in multiple scenarios based on different engine and vehicle models;

[0102] Acquisition unit 302 is used to acquire real-time data and scene information. The real-time data includes: vehicle data, engine data, DPF data, DOC data, and environmental data.

[0103] The search unit 303 is used to search for a risk model in the corresponding scenario based on the scenario information.

[0104] The first calculation unit 304 is used to input the real-time data into the risk model under the corresponding scenario, and calculate the target risk coefficient and the correction coefficient, wherein the target risk coefficient and the correction coefficient are in one-to-one correspondence.

[0105] The second calculation unit 305 is used to calculate the DPF regeneration risk coefficient based on the target risk coefficient and the correction coefficient.

[0106] Rating unit 306 is used to rate the DPF regeneration risk coefficient and obtain a rating result.

[0107] Control unit 307 is used to control the vehicle and the engine mode according to the rating result.

[0108] In this embodiment, the DPF on-the-go regeneration control device pre-establishes a risk model, then inputs real-time data into the risk model to obtain the target risk coefficient and correction coefficient, and then calculates the DPF regeneration risk coefficient based on the target risk coefficient and correction coefficient. This DPF regeneration risk coefficient fully considers various influencing factors in the real-time data and comprehensively analyzes the safety of DPF on-the-go regeneration. Furthermore, a risk level rating is performed based on the DPF regeneration risk coefficient, and the engine mode is determined by the risk rating, ensuring both the safety of the engine and the DPF, while also meeting the requirements of DPF on-the-go regeneration.

[0109] See Figure 4 , Figure 4 This is a schematic diagram of the structure of a DPF (Device Power Filter) vehicle regeneration control device according to another embodiment of this application. The DPF vehicle regeneration control device includes:

[0110] Establishment unit 301 is used to pre-establish risk models in multiple scenarios based on different engine and vehicle models;

[0111] The establishment unit 301 includes:

[0112] Model module 3011, which is used to obtain different engine models and vehicle models;

[0113] Combination module 3012, which is used to combine the engine model and the car model to obtain multiple combinations of engine and car;

[0114] Analysis module 3013 is used to analyze the risk level of various combinations of engines and vehicles in various practical scenarios;

[0115] Model module 3014, which is used to establish a risk model based on the risk level;

[0116] Acquisition unit 302 is used to acquire real-time data and scene information. The real-time data includes: vehicle data, engine data, DPF data, DOC data, and environmental data.

[0117] The search unit 303 is used to search for a risk model in the corresponding scenario based on the scenario information.

[0118] The first calculation unit 304 is used to input the real-time data into the risk model under the corresponding scenario, and calculate the target risk coefficient and the correction coefficient, wherein the target risk coefficient and the correction coefficient are in one-to-one correspondence.

[0119] The second calculation unit 305 is used to calculate the DPF regeneration risk coefficient based on the target risk coefficient and the correction coefficient.

[0120] Rating unit 306 is used to rate the DPF regeneration risk coefficient and obtain a rating result.

[0121] Control unit 307 is used to control the vehicle and the engine mode according to the rating result.

[0122] Optionally, the target risk coefficients include: vehicle speed risk coefficient, throttle risk coefficient, battery voltage risk coefficient, engine speed risk coefficient, circulating oil volume risk coefficient, coolant temperature risk coefficient, intercooler after-temperature risk coefficient, DOC inlet temperature risk coefficient, DPF inlet temperature risk coefficient, ambient temperature risk coefficient, and atmospheric pressure risk coefficient.

[0123] Optionally, the second calculation unit 305 is specifically used for:

[0124] Obtain the calculation formula, which is:

[0125] η=A×α+B×β+C×γ+D×δ+E×ε+F×ρ+G×θ+H×σ+I×μ+J×ρ+K×φ;

[0126] Where η represents the DPF regeneration risk coefficient, A represents the vehicle speed risk coefficient, B represents the throttle risk coefficient, C represents the battery voltage risk coefficient, D represents the engine speed risk coefficient, E represents the circulating oil quantity risk coefficient, F represents the coolant temperature risk coefficient, G represents the intercooler after-temperature risk coefficient, H represents the DOC inlet temperature risk coefficient, I represents the DPF inlet temperature risk coefficient, J represents the ambient temperature risk coefficient, and K represents the atmospheric pressure risk coefficient; α represents the correction coefficient corresponding to A, β represents the correction coefficient corresponding to B, γ represents the correction coefficient corresponding to C, δ represents the correction coefficient corresponding to D, ε represents the correction coefficient corresponding to E, ρ represents the correction coefficient corresponding to F, θ represents the correction coefficient corresponding to G, σ represents the correction coefficient corresponding to H, μ represents the correction coefficient corresponding to I, ρ represents the correction coefficient corresponding to J, and φ represents the correction coefficient corresponding to K.

[0127] The DPF regeneration risk coefficient is calculated according to the formula.

[0128] Optionally, the control unit 307 is specifically used for:

[0129] When the rating unit 306 determines that the rating result is high risk, the engine is kept in normal mode so that the vehicle exits DPF vehicle regeneration or the vehicle is prohibited from entering DPF vehicle regeneration.

[0130] Optionally, the control unit 307 is specifically used for:

[0131] When the rating unit 306 determines that the rating result is medium risk, it activates the engine's warm-up mode and prohibits the vehicle from entering the DPF driving regeneration mode.

[0132] Optionally, the control unit 307 is specifically used for:

[0133] When the rating unit 306 determines that the rating result is low risk, it activates the DOC heating mode of the engine to cause the vehicle to exit DPF vehicle regeneration or prevent the vehicle from entering DPF vehicle regeneration.

[0134] Optionally, the control unit 307 is specifically used for:

[0135] When the rating unit 306 determines that the rating result is risk-free, the engine is kept in driving regeneration mode, so that the vehicle enters or continues DPF driving regeneration.

[0136] The DPF vehicle regeneration control device in this embodiment analyzes and judges the state of DPF vehicle regeneration based on the specific engine and vehicle model and usage scenario, so that the engine and vehicle can control DPF vehicle regeneration in four modes respectively. Moreover, each operating mode has been specifically differentiated according to the corresponding risk level, thereby improving the accuracy and safety of DPF vehicle regeneration.

[0137] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of a DPF (Device Power Filter) vehicle regeneration control device according to another embodiment of this application. The DPF vehicle regeneration control device includes:

[0138] Processor 401, memory 402, input / output unit 403, bus 404;

[0139] The processor 401 is connected to the memory 402, the input / output unit 403, and the bus 404;

[0140] The memory 402 stores a program, and the processor 401 calls the program to execute it, such as... Figure 1 or Figure 2 The DPF (Device Filter) vehicle regeneration control method shown in the embodiment.

[0141] This application also relates to a computer-readable storage medium on which a program is stored, characterized in that, when the program is run on a computer, it causes the computer to perform actions such as... Figure 1 or Figure 2 The DPF (Device Filter) vehicle regeneration control method shown in the embodiment.

[0142] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0143] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, 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 an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0144] 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.

[0145] Furthermore, the functional units in the various embodiments of this application 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.

[0146] 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 this application, 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 this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A DPF (Driving Power Filter) regenerative control method for train operation, characterized in that, The method includes: Obtain different engine models and vehicle models; combine the engine models and vehicle models to obtain multiple combinations of engines and vehicles; analyze the risk level of multiple combinations of engines and vehicles under various practical scenarios; establish risk models based on the risk levels, the risk models including: vehicle speed range model, throttle range model, battery voltage range model, engine speed range model, circulating oil volume range model, coolant temperature range model, intercooler after-temperature range model, DOC before temperature range model, DPF before temperature range model, ambient temperature range model, and atmospheric pressure range model; Acquire real-time data and scene information, including: vehicle data, engine data, DPF data, DOC data, and environmental data; Find the risk model for the corresponding scenario based on the scenario information; The real-time data is input into the risk model under the corresponding scenario to calculate the target risk coefficient and the correction coefficient. The target risk coefficient and the correction coefficient are in one-to-one correspondence. The target risk coefficient includes: vehicle speed risk coefficient, throttle risk coefficient, battery voltage risk coefficient, engine speed risk coefficient, circulating oil volume risk coefficient, coolant temperature risk coefficient, intercooler after-temperature risk coefficient, DOC inlet temperature risk coefficient, DPF inlet temperature risk coefficient, ambient temperature risk coefficient, and atmospheric pressure risk coefficient. Obtain the calculation formula, which is: η=A×α+B×β+C×γ+D×δ+E×ε+F×ρ+G×θ+H×σ+I×μ+J×ρ+K×φ; in, The following are the risk factors for DPF regeneration: A represents the vehicle speed risk factor, B represents the throttle risk factor, C represents the battery voltage risk factor, D represents the engine speed risk factor, E represents the circulating oil volume risk factor, F represents the coolant temperature risk factor, G represents the intercooler after-temperature risk factor, H represents the DOC inlet temperature risk factor, I represents the DPF inlet temperature risk factor, J represents the ambient temperature risk factor, and K represents the atmospheric pressure risk factor; α represents the correction factor corresponding to A, β represents the correction factor corresponding to B, γ represents the correction factor corresponding to C, δ represents the correction factor corresponding to D, ε represents the correction factor corresponding to E, ρ represents the correction factor corresponding to F, ρ represents the correction factor corresponding to G, σ represents the correction factor corresponding to H, μ represents the correction factor corresponding to I, ρ represents the correction factor corresponding to J, and φ represents the correction factor corresponding to K; the DPF regeneration risk factor is calculated according to the above formula. The DPF regeneration risk coefficient is used to assign a rating, and the rating result is obtained. The vehicle and engine modes are controlled based on the rating results.

2. The DPF travel regeneration control method according to claim 1, characterized in that, The method of controlling the vehicle and the engine mode based on the rating result includes: When the rating result is high risk, the engine is kept in normal mode so that the vehicle exits DPF vehicle regeneration or is prohibited from entering DPF vehicle regeneration.

3. The DPF travel regeneration control method according to claim 1, characterized in that, The method of controlling the vehicle and the engine mode based on the rating result includes: When the rating result is medium risk, the engine's warm-up mode is activated, and the vehicle is prohibited from entering DPF driving regeneration.

4. The DPF travel regeneration control method according to claim 1, characterized in that, The method of controlling the vehicle and the engine mode based on the rating result includes: When the rating result is low risk, the engine's DOC heating mode is activated to cause the vehicle to exit DPF on-board regeneration or prevent the vehicle from entering DPF on-board regeneration.

5. The DPF travel regeneration control method according to claim 1, characterized in that, The method of controlling the vehicle and the engine mode based on the rating result includes: When the rating result is no risk, the engine is kept in driving regeneration mode, allowing the vehicle to enter or continue DPF driving regeneration.

6. A DPF (Device Filter) vehicle regeneration control device, characterized in that, include: A setup unit is used to acquire different engine models and vehicle models; and to combine the engine models and vehicle models to obtain multiple combinations of engines and vehicles. Analyze the risk levels of various engine and vehicle combinations in multiple practical scenarios; A risk model is established based on the risk level, and the risk model includes: vehicle speed range model, throttle range model, battery voltage range model, engine speed range model, circulating oil volume range model, coolant temperature range model, intercooler after-temperature range model, DOC before temperature range model, DPF before temperature range model, ambient temperature range model, and atmospheric pressure range model. The acquisition unit is used to acquire real-time data and scene information, wherein the real-time data includes: vehicle data, engine data, DPF data, DOC data and environmental data; A search unit, configured to search for a risk model for a corresponding scenario based on the scenario information; The first calculation unit is used to input the real-time data into the risk model under the corresponding scenario, and calculate the target risk coefficient and the correction coefficient. The target risk coefficient and the correction coefficient are in one-to-one correspondence. The target risk coefficient includes: vehicle speed risk coefficient, throttle risk coefficient, battery voltage risk coefficient, engine speed risk coefficient, circulating oil volume risk coefficient, coolant temperature risk coefficient, intercooler after-temperature risk coefficient, DOC inlet temperature risk coefficient, DPF inlet temperature risk coefficient, ambient temperature risk coefficient, and atmospheric pressure risk coefficient. The second calculation unit is used to obtain the calculation formula, which is: η=A×α+B×β+C×γ+D×δ+E×ε+F×ρ+G×θ+H×σ+I×μ+J×ρ+K×φ; in, The following are the risk factors for DPF regeneration: A represents the vehicle speed risk factor, B represents the throttle risk factor, C represents the battery voltage risk factor, D represents the engine speed risk factor, E represents the circulating oil volume risk factor, F represents the coolant temperature risk factor, G represents the intercooler after-temperature risk factor, H represents the DOC inlet temperature risk factor, I represents the DPF inlet temperature risk factor, J represents the ambient temperature risk factor, and K represents the atmospheric pressure risk factor; α represents the correction factor corresponding to A, β represents the correction factor corresponding to B, γ represents the correction factor corresponding to C, δ represents the correction factor corresponding to D, ε represents the correction factor corresponding to E, ρ represents the correction factor corresponding to F, ρ represents the correction factor corresponding to G, σ represents the correction factor corresponding to H, μ represents the correction factor corresponding to I, ρ represents the correction factor corresponding to J, and φ represents the correction factor corresponding to K; the DPF regeneration risk factor is calculated according to the above formula. A rating unit is used to rate the regeneration risk coefficient of the DPF and obtain a rating result. A control unit for controlling the vehicle and engine modes based on the rating results.

7. A device for DPF (Device Power Filter) travel regeneration control, characterized in that, The device includes: Processor, memory, input / output units, and bus; The processor is connected to the memory, the input / output unit, and the bus; The memory stores a program, which the processor calls to execute the DPF vehicle regeneration control method as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a program stored thereon, the program executing, when executed on a computer, the DPF vehicle regeneration control method as described in any one of claims 1 to 5.

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