Diesel oxidation catalyst temperature rise control method combined with prediction information

Abstract

The invention is suitable for the technical field of diesel engine aftertreatment system control, and provides a diesel oxidation catalyst temperature rise control method combined with prediction information, which comprises the following steps: acquiring a DPF regeneration instruction; acquiring macroscopic long-time-domain vehicle speed prediction information; constructing a regeneration energy consumption model oriented to energy consumption optimization control; constructing an energy consumption optimization management problem; solving by a DOC temperature rise control algorithm combined with prediction information; transmitting the solved control input sequence to a power execution control unit of the diesel engine post-processing system; feedback information of the post-processing system is obtained; and judging whether regeneration is quitted. According to the method, system lag can be quickly compensated, the regeneration target temperature can be accurately tracked, the equipment burning risk caused by too high temperature and the negative influence of low temperature on the regeneration efficiency are effectively avoided, and therefore the efficient and safe DPF regeneration process is achieved. Not only is the energy-saving potential in the regeneration process improved, but also the safety and the reliability of the equipment are remarkably enhanced.

Description

Technical Field

[0001] The present invention belongs to the technical field of diesel engine after-treatment system control, and particularly relates to a method for controlling the temperature rise of a diesel oxidation catalyst combined with prediction information. Background Art

[0002] The Diesel Oxidation Catalyst (DOC) and the Diesel Particulate Filter (DPF) system are the core components of modern diesel engine exhaust after-treatment technology, and they are arranged closely in sequence along the axis. The DOC is used to efficiently catalyze the oxidation of unburned hydrocarbons (HC) and carbon monoxide (CO). The DPF is widely regarded as an effective means to reduce the particulate matter (PM) emissions of diesel engines. It captures and stores the particulate matter in the exhaust gas through physical filtration, further purifying the emissions. Through the synergistic effect of the oxidation of the DOC and the physical filtration of the DPF, modern diesel engines can more effectively reduce the impact on the environment and contribute to green travel and sustainable development.

[0003] However, during the process of the DPF capturing particulate matter, the particulate layer on the inner channel wall of the DPF will gradually thicken, which may cause the blockage of the DPF, affect the performance of the engine, and lead to additional fuel consumption. To solve this problem, the DPF needs to be actively regenerated, that is, using the heat energy released by the oxidation reaction of the post-injected fuel on the surface of the DOC catalyst, under specific high-temperature conditions, to promote the oxidation reaction of the particulate matter adsorbed in the DPF, converting it into carbon dioxide and discharging it into the atmosphere. This regeneration process poses strict requirements on the temperature control of the DOC. If the temperature rise of the DOC is too low, it will lead to the prolongation of the regeneration process, which will not only increase fuel consumption but also have a negative impact on the driving experience; if the temperature rise of the DOC is too high, although the regeneration time can be shortened, it will significantly increase fuel consumption and may also damage the DPF device. Therefore, how to accurately track the temperature of the slow-varying system (DOC) under variable inputs to ensure the regeneration efficiency of the DPF has become the key challenge faced.

[0004] At present, the DOC heating-up technology is mainly divided into two categories. The first type of technology is the post-injection heating-up control strategy that integrates the feedforward and feedback mechanisms. It comprehensively considers the real-time state information at the DOC inlet at the current moment, and realizes the precise control and tracking of the DOC target temperature by accurately adjusting the amount and injection timing of the post-injection fuel. This method can exhibit relatively ideal performance under static conditions, but in transient conditions, due to the rapid fluctuations of the inlet state information (temperature and air flow rate), the heating-up inside the DOC is slow and there is a significant lag, resulting in unstable temperature control, easy increase in fuel consumption, and affecting fuel economy. The second type of technology is to enhance the temperature control stability of the DOC by introducing external auxiliary devices (such as electric heaters or auxiliary burners). Although this method has brought significant stability and efficiency improvement to the temperature control of the DOC, the addition of extra devices inevitably increases the complexity of the system, and due to the additional energy input and power consumption required for the operation of the devices, the overall energy consumption increases accordingly, which to a certain extent affects the further optimization of fuel economy. Therefore, how to achieve efficient, stable and low-energy DOC heating-up control under dynamic conditions is still an urgent problem to be solved. For this reason, the present invention proposes a method for controlling the heating-up of a diesel oxidation catalyst combined with prediction information. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for controlling the heating-up of a diesel oxidation catalyst combined with prediction information, aiming to solve the problems raised in the above background technology.

[0006] The purpose of the present invention is achieved through the following technical solutions:

[0007] A method for controlling the heating-up of a diesel oxidation catalyst combined with prediction information, comprising the following steps:

[0008] Step 1: Obtain the DPF regeneration instruction;

[0009] When the carbon loading level inside the DPF reaches the set value, start the control algorithm;

[0010] Step 2: Obtain the vehicle speed prediction information in the macroscopic long time domain;

[0011] Based on the intelligent cloud-connected system, obtain the road conditions ahead and the vehicle speed of the vehicle in front, plan the vehicle speed prediction information, and combine with the gear change to convert the vehicle speed prediction information into the engine speed and torque prediction information;

[0012] Step 3: Construct a regeneration energy consumption model for energy consumption optimization control, specifically including:

[0013] Step 3.1: Establish an exhaust gas flow rate and exhaust gas temperature estimation model;

[0014] Step 3.2: Establish a DOC control temperature model;

[0015] Step 4: Construct the energy consumption optimization management problem, specifically including:

[0016] Step 4.1: Establish the energy consumption management cost function;

[0017] Step 4.2: Determine the constraint conditions of the optimization problem;

[0018] Step 5: Solve using the DOC temperature rise control algorithm combined with prediction information, specifically including:

[0019] Step 5.1: Time discretization and acquisition of prediction information;

[0020] Step 5.5: Use the Fmincon optimization toolbox to solve the control input sequence within the prediction time domain;

[0021] Step 6: Transmit the obtained control input sequence to the power execution control unit of the diesel engine aftertreatment system;

[0022] Step 7: Obtain the feedback information of the aftertreatment system;

[0023] Step 8: Determine whether to exit the regeneration;

[0024] When the target regeneration end value or the target regeneration time value set by the controller is reached, exit the entire regeneration process.

[0025] Furthermore, in the said Step 3.1, the exhaust gas flow rate and temperature are estimated by looking up tables through the engine speed and torque, and the exhaust gas temperature change process is simulated as a first-order delay process, and the formula is as follows:

[0026]

[0027] Among them, the parameters satisfy the following equation:

[0028]

[0029] T exh (0) = T env

[0030] Among them, T exh is the transient estimated value of the exhaust gas temperature, representing the DOC inlet temperature; T env is the ambient temperature; T ss is the steady-state estimated value of the exhaust gas temperature; ΔT exh (k) is the change value of the exhaust gas temperature; φ is the engine empirical time constant, including ψ heat and ψ cool , and the two are respectively the empirical time constants related to the engine heating and cooling processes, ψ heat is represented in the form of a constant, ψ coolIt is expressed in the form of an exponential polynomial related to the engine speed, specifically as follows:

[0031]

[0032] Among them, p1 and p2 are parameters to be identified.

[0033] Furthermore, in the step 3.2, the differential equation of the DOC facing the control temperature model is as follows:

[0034]

[0035] Among them, ΔT out is the change in the DOC outlet temperature; is the gas mass flow rate; c p,wall is the specific heat capacity of the gas; T in (t) is the gas temperature at the DOC inlet; η comb (λ, T out ) is the combustion efficiency term; is the mass flow rate of the fuel, ΔH comb is the combustion calorific value of diesel; β fuel is a piecewise linear regulation coefficient; ε is the surface reflectivity; σ is the Stefan - Boltzmann constant; A wall is the effective surface area of the wall - gas contact; T env is the ambient temperature; c p,wall is the specific heat capacity of the DOC system; m wall is the mass of the DOC system; T s is the sampling time; T out (t) is the DOC outlet temperature at the current moment; T out (t + 1) is the DOC outlet temperature at the next moment. For the convenience of subsequent expression, it is organized into the following form:

[0036]

[0037] Furthermore, in the step 4.1, the optimization objective is to minimize the fuel consumption during the regeneration process on the basis of meeting the DOC heating rate and stability:

[0038]

[0039] Among them, J is the temperature deviation cost function of the system within the prediction horizon; T target is the target temperature of the DOC; T out (k + i|k) is the exhaust gas temperature of the DOC within the prediction horizon; u k The control input of the system represents the instantaneous fuel input at time t; is the value set of the system control input; N PRepresents the prediction horizon.

[0040] In step 4.2, to solve for the fuel consumption in the minimum regeneration process that satisfies the heating rate and stability, the following constraints need to be met:

[0041]

[0042] Where, T out (k + 1) represents the fuel consumption required for the system to increase temperature; T exh (k + i|k) represents the predicted sequence of the engine exhaust temperature; T in (k + i|k) represents the initial state value of the inlet temperature of the DOC system; u min and u max are respectively the upper and lower limits of the fuel injection quantity; u(k) represents the mass flow rate of the fuel.

[0043] Further, the specific operation of step 5.1 is: divide the prediction horizon into Np time periods with a fixed step size, and at each sampling moment, obtain the corresponding exhaust state and system state.

[0044] Further, the specific operation of step 5.2 is: when the prediction horizon has been discretized into equal time intervals, take the control inputs at each discrete moment as the sequence of decision variables to be optimized; based on the dynamic equation of the state variables, construct a cost function that describes the system performance index; Fmincon starts from the given initial guess value and continuously iteratively updates the control input sequence; at each iteration, Fmincon calculates the evolution process of the system state and its corresponding cost function value according to the current control sequence, and adopts an optimization strategy to gradually reduce the cost function value under the premise of satisfying the constraint conditions; when the convergence condition is met or the maximum number of iterations is reached, Fmincon outputs a set of optimal control input sequences to minimize the cumulative cost of the system within the entire prediction horizon, and thus obtains the first optimal control quantity corresponding to each prediction horizon as the actual input quantity.

[0045] Further, in step 7, the feedback information of the post-treatment system includes the real-time temperature information of the DOC system, the internal carbon loading information of the DPF system, and the NOx sensor information.

[0046] Compared with the prior art, the beneficial effects of the present invention are:

[0047] In view of the problem of frequent jitter in the inlet temperature of the diesel particulate filter (DPF) caused by disturbances, by predicting the dynamic changes in exhaust gas flow and temperature and using model predictive control to optimize the post-injection fuel quantity, precise and stable control of the DPF inlet temperature is achieved. This method can quickly compensate for system lag, accurately track the regeneration target temperature, effectively avoid the risk of equipment burnout caused by excessive temperature and the negative impact of low temperature on regeneration efficiency, thereby realizing an efficient and safe DPF regeneration process. This method not only improves the energy-saving potential during the regeneration process but also significantly enhances the safety and reliability of the equipment. Description of the Drawings

[0048] Figure 1 It is a flowchart of the method of the present invention.

[0049] Figure 2 It is the planned speed curve of the suburban area on the real road.

[0050] Figure 3 It is the planned mass flow curve of the suburban area on the real road.

[0051] Figure 4 It is the planned DOC inlet temperature curve of the suburban area on the real road.

[0052] Figure 5 It is the comparison chart of temperature control under the driving conditions of the suburban area on the real road.

[0053] Figure 6 It is the distribution chart of temperature tracking error.

[0054] Figure 7 It is the comparison chart of the total post-injection fuel consumption results after the regeneration process. Detailed Implementation Manner

[0055] For a clearer understanding of the technical features, objectives, and beneficial effects of the present invention, the technical solution of the present invention will be described in detail below, but it should not be construed as a limitation on the implementable scope of the present invention.

[0056] The present invention provides a method for controlling the temperature rise of a diesel oxidation catalyst combined with prediction information. Its flowchart is as Figure 1 shown, including the following steps:

[0057] Step 1: Obtain the DPF regeneration instruction;

[0058] The engine electronic control unit analyzes the carbon loading level inside the DPF based on parameters such as exhaust back pressure. When the set value is captured, a regeneration instruction is given and the algorithm starts running. This instruction is also the key to exiting the algorithm loop. When the engine electronic control unit determines that the internal carbon loading level is lower than the set value, the regeneration process is stopped and the algorithm operation is also stopped. On the premise of meeting the regeneration scenario target, the energy-saving target of the system regeneration process is achieved.

[0059] Step 2: Obtain the vehicle speed prediction information in the macroscopic long time domain;

[0060] Based on the intelligent cloud-connected system, obtain information such as the road condition ahead and the vehicle speed of the vehicle in front, and then plan the vehicle speed prediction information of the vehicle. Combining with the gear change, convert the vehicle speed prediction information into the engine speed and torque prediction information, and then provide dynamic input conditions for the regenerative control.

[0061] Step 3: Build a regenerative energy consumption model for energy consumption optimization control;

[0062] Step 3.1: Establish an exhaust gas flow and exhaust gas temperature estimation model;

[0063] The exhaust gas flow and temperature can be estimated by looking up tables through the engine speed and torque. Since the exhaust gas flow is the same under steady-state and transient conditions, the instantaneous exhaust gas flow data is equal to the MAP table data calibrated under steady state. There are significant differences in the exhaust gas temperature under steady state and transient conditions, so further correction is required. Simulate the exhaust gas temperature change process as a first-order delay process related to the engine component state. The design formula is as follows:

[0064] Equation 1:

[0065] Among them, the parameters satisfy the following equation:

[0066] Equation 2:

[0067] Among them, T exh is the transient estimated value of the exhaust gas temperature, representing the DOC inlet temperature; T env is the ambient temperature; T ss is the steady-state estimated value of the exhaust gas temperature, obtained from the calibrated MAP; ψ heat and ψ cool are the empirical time constants related to the engine heating and cooling processes respectively; ΔT exh (k) is the change value of the exhaust gas temperature; φ is the engine empirical time constant. According to the statistical analysis of the test data, the time constant of the heating process is relatively stable, while the time constant of the cooling process is significantly affected by the engine speed ω. Therefore, ψ heat is represented in the form of a constant, while ψ cool is represented in the form of an exponential polynomial related to the engine speed, as shown below:

[0068] Equation 3:

[0069] Among them, p1 and p2 are the parameters to be identified.

[0070] Based on the above formula, the estimation of the steady-state temperature to the transient temperature can be realized, and then the high-precision transient DOC inlet data can be obtained to realize the prediction of the input information sequence.

[0071] Step 3.2: Establish a DOC temperature model for control;

[0072] First, according to the law of conservation of energy:

[0073] Equation 4: Q total = Q gas-wall + Q comb - Q rad

[0074] Among them, Q total is the heat of the entire stage, Q gas-wall is the heat transferred from the DOC gas to the wall, Q comb is the heat generated by diesel combustion, and Q rad represents the heat dissipated in the entire reaction.

[0075] Assume that the DOC outlet gas temperature is T out , that is, the inlet temperature of the DPF. Reasonably simplifying the heat dissipation of this section of the pipe, this temperature can be regarded as the actual temperature of DPF regeneration, that is, the temperature to be adjusted in the present invention. The mass of the DOC system is m wall , and its specific heat capacity is c p,wall . According to the heat conduction equation:

[0076] Equation 5:

[0077] The equation can be rewritten as:

[0078] Equation 6:

[0079] (1) The convective heat transfer term between the gas and the wall;

[0080] The gas flow in the DOC will transfer heat to the wall, and the heat transfer amount can be expressed by the convective heat transfer formula:

[0081] Equation 7:

[0082] Among them, is the gas mass flow rate; c p,wall is the gas specific heat capacity; T in (t) is the gas temperature at the DOC inlet; T out (t) is the DOC outlet gas temperature. When T in (t)>T out (t), the gas transfers heat to the wall, increasing the wall temperature, and vice versa, reducing the wall temperature.

[0083] (2) Diesel combustion heat generation term;

[0084] In the combustion reaction, the heat released when diesel is completely burned is determined by the combustion calorific value ΔH of diesel comb and the mass flow rate of the fuel as shown in Equation 8:

[0085] Equation 8:

[0086] In actual situations, the combustion process is not completely efficient, and the combustion efficiency is affected by various factors, especially the excess air coefficient λ and the outlet gas temperature T out . Therefore, the present invention introduces a combustion efficiency term η comb (λ, T out ), and the heat generated by actual combustion needs to be multiplied by the combustion efficiency:

[0087] Equation 9:

[0088] Equation 9 reflects the actual heat released when the combustion efficiency is less than 100% in actual situations.

[0089] Under specific working conditions, increasing the fuel injection amount does not always show a linear growth relationship with the heat generated by combustion. The reason for this phenomenon is that when the fuel injection amount exceeds a certain critical point, it may lead to incomplete combustion. In this case, due to the excessive fuel not being fully burned, part of the fuel may be discharged in an unburned form, or a rich mixture may be formed in the combustion chamber, and these factors may all inhibit the combustion efficiency. To simulate this phenomenon, the present invention introduces a non-linear adjustment coefficient β fuel to control the non-linear effect of the fuel injection amount on the combustion heat. Placing β fuel in the denominator can prevent the heat from increasing linearly without limit when the fuel injection amount increases too much.

[0090] Therefore, the final equation for diesel combustion heat generation can be obtained as:

[0091] Equation 10:

[0092] (3) Gas and wall radiation heat dissipation term;

[0093] The present invention introduces a radiation heat dissipation term Q rad , which is derived from the Stefan-Boltzmann Law. The Stefan-Boltzmann Law describes the relationship between the radiation power per unit area on the surface of a black body and the temperature, as shown in Equation 11:

[0094] Equation 11: q = σT 4

[0095] Among them, q is the radiant heat; σ is the Stefan-Boltzmann constant; T is the absolute temperature of the object.

[0096] In actual situations, the object is usually not an ideal black body but a gray body. The radiation ability of a gray body is lower than that of a black body, and its radiation ability is adjusted by the surface reflectivity ε, where ε is a dimensionless coefficient between 0 and 1. The calculation formula for the radiant heat of a gray body is:

[0097] Equation 12: q = εσT 4

[0098] (4) Radiative heat dissipation between the wall surface and the environment;

[0099] When the wall surface temperature and the environment temperature are different, the wall surface will transfer heat to the environment through radiation, and the heat dissipation in this process can be expressed as:

[0100] Equation 13:

[0101] Among them, A wall is the effective surface area of the wall surface in contact with the gas; T out is the outlet gas temperature; T env is the environment temperature.

[0102] Substituting Equation 7, Equation 10, Equation 12, and Equation 13 into Equation 4, the final differential equation can be obtained as shown in Equation 14:

[0103] Equation 14:

[0104]

[0105] Among them, ΔT out is the change in the DOC outlet temperature; is the gas mass flow rate; c p,wall is the specific heat capacity of the gas; T in (t) is the gas temperature at the DOC inlet; η comb (λ, T out ) is the combustion efficiency term; is the mass flow rate of the fuel, ΔH comb is the combustion calorific value of diesel; β fuel is a non-linear adjustment coefficient; ε is the surface reflectivity; σ is the Stefan-Boltzmann constant; A wall is the effective surface area of the wall surface in contact with the gas; T env is the environment temperature; c p,wall is the specific heat capacity of the DOC system; m wall is the mass of the DOC system; T s is the sampling time; T out (t) is the DOC outlet temperature at the current moment; Tout (t + 1) is the DOC outlet temperature at the next moment. For the convenience of subsequent expression, it is organized into the following form:

[0106] Equation 15:

[0107] Step 4: Construct the energy consumption optimization management problem;

[0108] Step 4.1: Establish the energy consumption management cost function;

[0109] The optimization goal is to minimize the fuel consumption during the regeneration process on the basis of meeting the DOC heating rate and stability:

[0110] Equation 16:

[0111] Among them, J is the temperature deviation cost function of the system within the prediction horizon; T target is the target temperature of the DOC; T out (k + i|k) is the exhaust gas temperature of the DOC within the prediction horizon; u k The control input of the system represents the instantaneous fuel input at time t; is the value set of the system control input; N P represents the prediction horizon.

[0112] Step 4.2: Determine the constraint conditions of the optimization problem;

[0113] To solve for the minimum fuel consumption during the regeneration process that meets the heating rate and stability, the following constraint conditions need to be satisfied:

[0114] Equation 17:

[0115] Among them, T out (k + 1) represents the fuel consumption required for the system to heat up; T exh (k + i|k) represents the predicted sequence of engine exhaust gas temperatures; T in (k + i|k) represents the initial state value of the inlet temperature of the DOC system; u min and u max are the upper and lower limits of the fuel injection quantity respectively; u(k) represents the mass flow rate of the fuel.

[0116] Step 5: Solve the DOC heating control algorithm by combining the prediction information;

[0117] Step 5.1: Time discretization and prediction information acquisition;

[0118] Time-discretize the predicted exhaust gas flow rate data and predicted exhaust gas temperature data within a future period of time, and divide them into multiple equally spaced sampling time points (for example, divide the prediction horizon into N at a fixed step size P(in each time period). At each sampling moment, the corresponding exhaust gas state (such as mass flow rate, temperature) and system state (DOC wall temperature) can be obtained, providing input conditions for subsequent dynamic programming.

[0119] Step 5.2: Use the Fmincon optimization toolbox to solve the control input sequence within the prediction horizon;

[0120] On the premise that the prediction horizon has been discretized into equal time intervals, the control inputs (fuel injection amounts) at each discrete moment are regarded as the decision variable sequence to be optimized. According to the dynamic equation of the state variables, a cost function describing the system performance index is established. Based on the given initial guess value, Fmincon continuously iteratively updates the control input sequence; in each iteration, Fmincon calculates the evolution process of the system state and its corresponding cost function value according to the current control sequence, and uses optimization strategies such as the interior point method to gradually reduce the cost function value under the premise of satisfying the constraint conditions; through multiple iterations, when the convergence condition is met or the maximum number of iterations is reached, Fmincon outputs a set of optimal control input sequences to minimize the cumulative cost (such as total fuel consumption) of the system within the entire prediction horizon, and thus obtains the first optimal control quantity corresponding to each prediction horizon as the actual input quantity.

[0121] Step 6: Transmit the solved control input sequence to the power execution control unit of the diesel engine aftertreatment system;

[0122] Transmit the optimal control variable u(k) in the DOC temperature rise control system to the vehicle's execution control unit, which acts on the engine fuel injector to achieve precise control of the DOC temperature.

[0123] Step 7: Obtain the feedback information of the aftertreatment system;

[0124] By continuously updating the real-time temperature information of the DOC system, reduce the cumulative error caused by model error and random disturbance problems, and ensure the accuracy of temperature control; obtain the carbon loading information inside the DPF system, and combine the regeneration rule and the carbon loading estimation model to judge the progress of regeneration; obtain the NOx sensor information to judge the emission reduction ability and whether the pollutant emissions meet the regulations.

[0125] Step 8: Judge whether to exit the regeneration;

[0126] According to the target regeneration end value or target regeneration time value set by the controller, timely exit the entire regeneration process to prevent fuel waste and achieve the purpose of saving fuel consumption and extending the service life of the equipment.

[0127] The following describes the specific implementation of the present invention in detail with specific embodiments.

[0128] Example 1: To prove the feasibility of the method of the present invention and evaluate the accuracy and energy-saving effect of the method of the present invention, real driving road test data is used as the inlet state information.

[0129] Select the suburban vehicle speed planning curve of a real road ( Figure 2 ) as the known information and input it into the estimation system, including steady driving, sudden acceleration, sudden deceleration, and idling conditions, highly restoring the random scenarios during vehicle driving, and interpreting it as the DOC inlet temperature and exhaust gas flow ( Figure 4 and Figure 3 ), and both are used as the key input parameters of the controller.

[0130] According to the Figure 4 shown temperature result analysis, it is found that the DOC inlet temperature (between 500K - 750K) is significantly lower than the regeneration target temperature range (above 873K), and the DPF regeneration process cannot be realized. Therefore, a temperature increase control strategy needs to be adopted in the DOC to increase the outlet temperature and achieve DPF soot loading cleaning. The temperature tracking comparison results are as Figure 5 shown. When the target temperature (Ref) is set to 873K, the temperature fluctuation range of traditional PID control is ±12K, while the algorithm proposed by the present invention controls the temperature fluctuation within ±8K. Extract data points according to the sampling step, and organize the temperature tracking errors of each sampling point into a histogram. The results are as Figure 6 shown. It can be seen from the figure that the algorithm (MPC) proposed by the present invention achieves a smaller temperature overshoot and has a higher tracking accuracy. In contrast, due to the slow-varying characteristics of the system, PID control cannot respond to dynamic input changes in a timely manner, resulting in larger temperature fluctuations. Under the suburban driving conditions of a real road, the total fuel consumption of the MPC and PID algorithms during the DPF regeneration process is 0.571kg and 0.578kg respectively ( Figure 7 ), and the MPC algorithm reduces fuel consumption by 1.2%.

[0131] Conclusion: The method of the present invention significantly reduces the target temperature tracking deviation caused by inlet temperature fluctuations and engine exhaust gas flow changes by predicting the future working state of the vehicle in advance, thereby greatly improving the regeneration efficiency of the DPF system during the regeneration process and reducing fuel waste caused by frequent temperature increases. In addition, the lower temperature fluctuations can effectively prevent equipment overheating and sintering phenomena caused by problems such as sudden engine misfires. In summary, the method of the present invention realizes precise temperature tracking by controlling the HC injector of the DOC sub-component of the aftertreatment system, fully considering the slow-varying characteristics of the DOC system and combining prediction information, while reducing energy consumption and improving performance.

[0132] The above are only the preferred embodiments of the present invention. It should be noted that for those skilled in the art, without departing from the concept of the present invention, several modifications and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the implementation effect of the present invention and the practicability of the patent.

Claims

1. A method for controlling the temperature rise of a diesel oxidation catalyst by combining prediction information, characterized in that It includes the following steps: Step 1: Obtain the DPF regeneration instruction; When the carbon loading level inside the DPF reaches the set value, start the control algorithm; Step 2: Obtain the vehicle speed prediction information in the long-term macro time domain; Based on the intelligent cloud-connected system, obtain the road conditions ahead and the vehicle speed of the vehicle in front, plan the vehicle speed prediction information, and combine with the gear change to convert the vehicle speed prediction information into the engine speed and torque prediction information; Step 3: Build a regeneration energy consumption model for energy consumption optimization control, specifically including: Step 3.1: Establish an exhaust gas flow and exhaust gas temperature estimation model; Step 3.2: Establish a DOC control temperature model; Step 4: Build an energy consumption optimization management problem, specifically including: Step 4.1: Establish an energy consumption management cost function; Step 4.2: Determine the constraint conditions of the optimization problem; Step 5: Solve the DOC temperature increase control algorithm combined with the prediction information, specifically including: Step 5.1: Time discretization and prediction information acquisition; Step 5.5: Use the Fmincon optimization toolbox to solve the control input sequence in the prediction time domain; Step 6: Transmit the obtained control input sequence to the power execution control unit of the diesel engine aftertreatment system; Step 7: Obtain the feedback information of the aftertreatment system; Step 8: Determine whether to exit the regeneration; When the target regeneration end value or the target regeneration time value set by the controller is reached, exit the entire regeneration process.

2. The method for controlling the temperature rise of a diesel oxidation catalyst combined with prediction information according to claim 1, wherein In the above step 3.1, the exhaust gas flow and temperature are estimated by looking up tables through the engine speed and torque. The exhaust gas temperature change process is simulated as a first-order delay process, and the formula is as follows: Among them, the parameters satisfy the following equation: where, T exh is the transient estimated value of the exhaust gas temperature, representing the DOC inlet temperature; T env is the ambient temperature; T ss is the steady-state estimated value of the exhaust gas temperature; ΔT exh (k) is the change value of the exhaust gas temperature; φ is the engine empirical time constant, including ψ heat and ψ cool , which are respectively the empirical time constants related to the engine heating and cooling processes, ψ heat is expressed in the form of a constant, ψ cool is expressed in the form of an exponential polynomial related to the engine speed, as shown below: Among them, p1 and p2 are the parameters to be identified.

3. The method for controlling the temperature rise of a diesel oxidation catalyst in combination with prediction information according to claim 1, wherein In the above step 3.2, the differential equation of the DOC control temperature model is as follows: Among them, ΔT out is the change in DOC outlet temperature; is the gas mass flow rate; c p,wall is the specific heat capacity of the gas; T in (t) is the gas temperature at the DOC inlet; η comb (λ, T out ) is the combustion efficiency term; is the mass flow rate of the fuel, ΔH comb is the calorific value of diesel combustion; β fuel is a piecewise linear adjustment coefficient; ε is the surface reflectivity; σ is the Stefan-Boltzmann constant; A wall is the effective surface area of contact between the wall and the gas; T env is the ambient temperature; c p,wall is the specific heat capacity of the DOC system; m wall is the mass of the DOC system; T s is the sampling time; T out (t) is the DOC outlet temperature at the current moment; T out (t + 1) is the DOC outlet temperature at the next moment; After being arranged into the following form:

4. The method for controlling the temperature rise of a diesel oxidation catalyst combined with prediction information according to claim 1, characterized in that, characterized in that, In the above step 4.1, the optimization goal is to minimize the fuel consumption during the regeneration process on the basis of meeting the DOC temperature increase speed and stability; where J is the temperature deviation cost function of the system within the prediction horizon; T target is the target temperature of the DOC; T out (k + i|k) is the exhaust gas temperature of the DOC within the prediction horizon; u k The control input of the system represents the instantaneous fuel input at time t; is the value set of the control input of the system; N P represents the prediction horizon.

5. The method for controlling the temperature rise of a diesel oxidation catalyst combined with prediction information according to claim 1, wherein In the above step 4.2, to solve for the minimum fuel consumption during the regeneration process that meets the temperature increase speed and stability, the following constraint conditions need to be satisfied: Among them, T out (k + 1) represents the fuel consumption required for the system to increase temperature; T exh (k + i|k) represents the predicted sequence of the engine exhaust temperature; T in (k + i|k) represents the initial state value of the inlet temperature of the DOC system; u min and u max are the upper and lower limits of the fuel injection quantity respectively; u(k) represents the mass flow rate of the fuel.

6. The method for controlling the temperature rise of a diesel oxidation catalyst by combining prediction information according to claim 1, wherein The specific operation of the above step 5.1 is: divide the prediction time domain into Np time periods with a fixed step size. At each sampling moment, obtain the corresponding exhaust gas state and system state.

7. The method for controlling the temperature rise of a diesel oxidation catalyst combined with prediction information according to claim 1, wherein The specific operation of the above step 5.2 is: when the prediction time domain has been discretized into equal time intervals, take the control inputs at each discrete moment as the decision variable sequence to be optimized; according to the dynamic equation of the state variables, build a cost function describing the system performance index; Fmincon starts from the given initial guess value and continuously iteratively updates the control input sequence; at each iteration, Fmincon calculates the evolution process of the system state and its corresponding cost function value according to the current control sequence, and adopts an optimization strategy to gradually reduce the cost function value on the premise of meeting the constraint conditions; when the convergence condition is met or the maximum number of iterations is reached, Fmincon outputs a set of optimal control input sequences to minimize the cumulative cost of the system within the entire prediction time domain, and thus obtains the first optimal control quantity corresponding to each prediction time domain as the actual input quantity.

8. The method for controlling the temperature rise of a diesel oxidation catalyst in combination with prediction information according to claim 1, characterized in that, In the said step 7, the feedback information of the post-treatment system includes the real-time temperature information of the DOC system, the internal carbon loading information of the DPF system, and the NOx sensor information.

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