Optimization control method and device for SCR target ammonia storage amount and engine controller

Abstract

The present invention provides a SCR The optimization control method, device and engine controller of the target ammonia storage amount belong to the technical field of engine exhaust purification. The method comprises: obtaining a control sequence at the current moment; the control sequence is used to control SCR The ammonia storage capacity of the system; the control sequence based on the current moment and the preset SCR Dynamic model and corresponding objective function to determine the prediction time domain SCR The state quantity and control target of the system; based on the state quantity and control target, determine the gradient of the control target to the control sequence at the current moment; based on the gradient and combined with the gradient descent iteration method and iteration step size, determine the optimal control sequence in the prediction time domain, and based on the optimal control sequence SCR The target ammonia storage capacity of the system is controlled. The present invention determines the optimal control sequence in the prediction time domain through the gradient descent iteration method, realizes the closed-loop optimization control strategy based on the model, and solves the problem of SCR Technical issues to improve the precision and accuracy of ammonia storage control.

Description

Technical Field

[0001] The present invention relates to the technical field of engine exhaust purification, and in particular to a SCR Target ammonia storage optimization control method, device and engine controller. Background Art

[0002] At present, in order to meet the requirements of the National VI emission regulations, the market is using SCR (Selective Catalytic Reduction Technology) Ammonia Storage Control + Post NO X Corrected SCR Closed-loop control scheme, SCR The ammonia storage control is based on the set target ammonia storage amount and the actual storage NH The deviation between the three quantities is controlled by PID, and the feedforward NH 3. Real-time adjustment and compensation of urea injection amount based on demand, so that SCR Catalyst storage NH 3. Achieve the target storage NH 3. The target ammonia storage capacity in the above-mentioned National VI control algorithm all adopts a MAP-based control strategy, which requires a large amount of experimental calibration to achieve. The transient control effect is poor, and the control function cannot be optimized and upgraded according to actual operating conditions. The ammonia storage control accuracy of SCR needs to be improved. Summary of the Invention

[0003] In view of this, it is necessary to provide a SCR Target ammonia storage optimization control method, device and engine controller are used to solve SCR The problem of improving the ammonia storage control accuracy.

[0004] In order to solve the above problems, the present invention provides a SCR The optimization control method for the target ammonia storage capacity includes:

[0005] Get the control sequence at the current moment; the control sequence is used to control SCR Ammonia storage capacity of the system;

[0006] Based on the control sequence at the current moment and the preset SCR Dynamic model and corresponding objective function to determine the prediction time domain SCR The state quantity and control target of the system;

[0007] Determining, based on the state quantity and the control target, a gradient of the control target with respect to the control sequence at a current moment;

[0008] Based on the gradient and in combination with the gradient descent iterative method and the iterative step size, the optimal control sequence of the prediction time domain is determined, and the optimal control sequence is used to determine the optimal control sequence of the prediction time domain. SCRThe target ammonia storage capacity of the system is controlled.

[0009] In a possible implementation, the iteration step size is inversely correlated with the number of iterations of the gradient descent iterative method, and when the number of iterations is greater than a preset threshold, the iteration step size remains unchanged.

[0010] In a possible implementation, the SCR The state of the system includes: SCR System export NOx Concentration and NH 3 concentration; SCR Kinetic models, including: NOx Kinetic models and NH 3. Kinetic model;

[0011] described NOx Dynamic model, used to characterize the current moment SCR System export NOx Concentration and last moment SCR System export NOx The relationship between concentrations;

[0012] described NH 3 Dynamic model, used to characterize the current moment SCR System export NH 3Concentration and last moment SCR System export NH 3. The relationship between concentrations.

[0013] In a possible implementation, the NOx The relationship represented by the kinetic model is:

[0014] Current moment SCR System export NOx Concentration is based on the previous moment SCR System export NOx Concentration, as well as control cycle, air speed, current time SCR System entrance NOx concentration, NOx Catalytic reduction reaction rate constant, NOx Activation energy of catalytic reduction reaction, SCR Catalyst temperature, last moment SCR The ammonia coverage of the system and SCR The maximum ammonia coverage of the system is determined.

[0015] In a possible implementation, the NH 3 The relationship represented by the kinetic model is:

[0016] Current moment SCR System exportNH 3 concentration, based on the last moment SCR System export NH 3 concentration, as well as control cycle, air speed, current time SCR System inlet urea injection amount, ammonia desorption reaction rate constant, ammonia adsorption reaction rate constant, NH 3Desorption reaction activation energy, last moment SCR Ammonia coverage of the system, NH 3. Desorption reaction characteristic parameters, SCR The system's maximum ammonia coverage and SCR The catalyst temperature is determined.

[0017] In a possible implementation, the objective function is based on the control period SCR System export NOx concentration, NH 3. Determine the leakage amount and the rate of change of the control sequence.

[0018] In a possible implementation, the objective function is to control the SCR System export NOx concentration, NH 3. The minimum weighted sum of leakage amount and control sequence change rate is the control target.

[0019] On the other hand, the present invention also provides a SCR The optimization control device for the target ammonia storage capacity includes:

[0020] The acquisition module is used to obtain the control sequence at the current moment; the control sequence is used to control SCR Ammonia storage capacity of the system;

[0021] Prediction module, used for control sequence based on current moment and preset SCR Dynamic model and corresponding objective function to determine the prediction time domain [[ID= The state quantity and control target of the system;

[0022] A gradient solving module, configured to determine, based on the state quantity and the cost, a gradient of the cost with respect to the control sequence at a current moment;

[0023] A control module is used to determine the optimal control sequence of the prediction time domain based on the gradient and in combination with the gradient descent iterative method and the iteration step size, and to ​ The target ammonia storage capacity of the system is controlled.

[0024] On the other hand, the present invention also provides an engine controller, comprising a memory and a processor, wherein:

[0025] The memory is used to store programs;

[0026] The processor is coupled to the memory and is configured to execute the program stored in the memory to implement any one of the above ​ Steps of the optimization control method for target ammonia storage.

[0027] On the other hand, the present invention also provides a car, comprising the above-mentioned engine controller.

[0028] The beneficial effects of adopting the above implementation method are: ​ Target ammonia storage optimization control method, device and engine controller, through the current moment ​ The control sequence of the system and the preset ​ Dynamic model and corresponding objective function to determine the prediction time domain ​ The state quantity and control target of the system, based on the state quantity and the control target, determine the gradient of the control target to the control sequence at the current moment; based on the gradient and combined with the gradient descent iterative method and iterative step size, determine the optimal control sequence in the prediction time domain, and realize the closed-loop optimization control strategy based on the model. ​ Kinetic models are predictive ​ The future state quantity of the system is based on the gradient and combined with the gradient descent iterative method and iterative step size, and the control action and feedback correction of the model error are repeatedly optimized and calculated online and rolled. The future output of the controlled object is predicted by the model, and the behavior of the system is evaluated using the relevant objective function. The optimal control sequence in the future period of time is solved by online rolling optimization. The online rolling optimization provided by the present invention is used to solve the optimal control sequence. The model predictive control has the advantages of good control effect and strong robustness. It can effectively overcome the uncertainty, nonlinearity and parallelism of the process, improve the transient control effect, and optimize and upgrade the control function according to the actual working conditions, thereby improving ​ Ammonia storage control accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0030] ​ The present invention provides ​ A flow chart of an embodiment of a method for optimizing and controlling target ammonia storage capacity;

[0031] ​ The present invention provides ​Logic diagram of the optimization control method for target ammonia storage;

[0032] ​ The present invention provides ​ A flow chart of another embodiment of a method for optimizing and controlling a target ammonia storage amount;

[0033] ​ The present invention provides ​ Principle block diagram of the optimization control device for target ammonia storage capacity;

[0034] ​ This is a schematic structural diagram of an embodiment of the engine controller provided by the present invention. DETAILED DESCRIPTION

[0035] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0036] In the description of the embodiments of the present application, unless otherwise specified, “a plurality of” means two or more.

[0037] The terms "including" and "having" and any variations thereof in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product or device comprising a series of steps or modules is not necessarily limited to those steps or modules explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products or devices.

[0038] The naming or numbering of the steps in the embodiments of the present invention does not mean that the steps in the method flow must be executed in the time / logical sequence indicated by the naming or numbering. The execution order of the named or numbered process steps can be changed according to the technical purpose to be achieved, as long as the same or similar technical effects can be achieved.

[0039] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0040] The present invention provides a ​The optimization control method, device and engine controller for the target ammonia storage amount are described below.

[0041] like ​ As shown, the present invention provides a ​ The optimization control method for the target ammonia storage capacity includes:

[0042] S101, obtain the control sequence at the current moment; the control sequence is used to control ​ Ammonia storage capacity of the system;

[0043] S102, based on the control sequence at the current moment and the preset ​ Dynamic model and corresponding objective function to determine the prediction time domain ​ The state quantity and control target of the system;

[0044] S103, determining a gradient of the control target with respect to a control sequence at a current moment based on the state quantity and the control target;

[0045] S104, based on the gradient and in combination with the gradient descent iterative method and the iterative step size, determine the optimal control sequence of the prediction time domain, and ​ The target ammonia storage capacity of the system is controlled.

[0046] It is understood that the control sequence in the present invention is also referred to as a control parameter or a control variable; ​ , namely selective catalytic reduction technology, is a technology for reducing the exhaust gas from diesel vehicles. ​ A treatment process, that is, under the action of a catalyst, ammonia or urea is injected as a reducing agent to reduce the ​ Restore to N 2 and H 2 O ; ​ The logic of the optimization control of the target ammonia storage capacity can be referred to ​ As shown, the target storage ​ 3. Amount is storage ​ 3. The control objective of closed-loop control. In order to ensure ​ The system has a higher ​ Conversion efficiency requires the storage ​ 3. The volume remains at a high level, but the reserves ​ 3. Too high a dose. ​ 3 The risk of escape will also increase greatly. ​ 3. Setting the quantity at a reasonable level is the key to ​ 3. The key to the control function. In order to achieve high-precision ammonia storage control, it is necessary to obtain real-time target ammonia storage amount calculation, thereby improving the accuracy of the final urea injection control.

[0047] The technical problem to be solved by this invention is to solve the problem of ​ Target ammonia storage optimization control provides a more accurate calculation ​ Target ammonia storage capacity calculation method for ammonia storage control. Model predictive control is a model-based control algorithm and closed-loop optimization control strategy. The core of the algorithm is: a predictable dynamic model, online repeated optimization calculation and rolling implementation of control actions, and feedback correction of model errors.

[0048] pass ​ Kinetic model (i.e. ​ Model predictive control (MPC) uses a chemical reaction kinetics model to predict the future output of the controlled object, evaluates system behavior using correlation functions, and solves the optimal control sequence for a specific period of time. MPC offers advantages such as effective control and strong robustness. It effectively overcomes process uncertainty, nonlinearity, and parallelism, and can easily handle various constraints on the controlled and manipulated variables.

[0049] right ​ Dynamic model optimization solution:

[0050] In view of this optimization problem, the present invention adopts the gradient descent method to solve it. The gradient descent method is a first-order optimization algorithm that uses the gradient descent method to find the local minimum of a function to improve the real-time performance of the operation. ​ The chemical reaction kinetics model, combined with the system input, can know the system state at each stage. The gradient descent method is used to solve it and obtain the optimal local control performance.

[0051] In some embodiments, the iteration step size is inversely correlated with the number of iterations of the gradient descent iterative method, and when the number of iterations is greater than a preset threshold, the iteration step size remains unchanged.

[0052] It is understandable that in order to accelerate the convergence process, the initial step size is set to be larger, and as the iteration process progresses, the step size is gradually reduced, and the step size is stabilized after N iterations, where N is a preset threshold. In some embodiments, the ​ The state of the system includes: ​ System export ​ Concentration and ​ 3 concentration; ​ Kinetic models, including: ​ Kinetic models and ​ 3. Kinetic model;

[0053] described ​ Dynamic model, used to characterize the current moment ​ System export ​ Concentration and last moment​ System export ​ The relationship between concentrations;

[0054] described ​ 3 Dynamic model, used to characterize the current moment ​ System export ​ 3Concentration and last moment ​ System export ​ 3. The relationship between concentrations.

[0055] In some embodiments, the ​ The relationship represented by the kinetic model is:

[0056] Current moment ​ System export ​ Concentration is based on the previous moment ​ System export ​ Concentration, as well as control cycle, air speed, current time ​ System entrance ​ concentration, ​ Catalytic reduction reaction rate constant, ​ Activation energy of catalytic reduction reaction, ​ Catalyst temperature, last moment ​ The ammonia coverage of the system and ​ The maximum ammonia coverage of the system is determined.

[0057] In some embodiments, the ​ 3 The relationship represented by the kinetic model is:

[0058] Current moment ​ System export ​ 3 concentration, based on the last moment ​ System export ​ 3 concentration, as well as control cycle, air speed, current time ​ System inlet urea injection amount, ammonia desorption reaction rate constant, ammonia adsorption reaction rate constant, ​ 3Desorption reaction activation energy, last moment ​ Ammonia coverage of the system, ​ 3. Desorption reaction characteristic parameters, ​ The system's maximum ammonia coverage and ​ The catalyst temperature is determined.

[0059] It is understood that the present invention is based on ​ Chemical reaction kinetics model calculations obtain basic control parameter inputs to ​ exit ​ Minimum concentration, ​ 3. Minimum leakage is the control target, target ammonia storage capacity is selected as the control variable, and the​ The target ammonia storage optimization solution control function. The specific implementation steps include the following parts:

[0060] Build ​ Chemical reaction kinetics model:

[0061] ​ exit ​ , ​ 3 Kinetic model calculation is as follows:

[0062]

[0063] Among them: state variables , control variables .

[0064] k is the sampling sequence, k=1,2,3,4,5………;

[0065] C NOx ( k )for k time ​ exit ​ concentration;

[0066] C NH3 ( k )for k time ​ exit ​ 3 concentration;

[0067] C NOX,in ( k )for k time ​ Entrance ​ concentration;

[0068] C NH3，in ( k )for k time ​ Inlet urea injection amount; ​ yes ​ Ammonia coverage of the system, ​ max for ​ Maximum ammonia coverage of the system;

[0069] ​ is the sampling time or control period;

[0070] is the airspeed, is the exhaust flow rate, V is the catalyst volume;

[0071] k 1 is the ammonia adsorption reaction rate constant; k 2 is the ammonia desorption reaction rate constant; k 3 for ​ Catalytic reduction reaction rate constant; E 1 for ​ 3. Activation energy of adsorption reaction;

[0072] E 2 for ​ 3. Activation energy of desorption reaction;

[0073] E 3 for ​ Activation energy of catalytic reduction reaction;

[0074] m for ​ 3. Desorption reaction characteristic parameters;

[0075] T for ​ Catalyst temperature, generally taken as ​ The average of the inlet and outlet temperatures.

[0076] In some embodiments, the objective function is based on the control period ​ System export ​ concentration, ​ 3. Determine the leakage amount and the rate of change of the control sequence.

[0077] In some embodiments, the objective function is to control the cycle ​ System export ​ concentration, ​ 3. The minimum weighted sum of leakage amount and control sequence change rate is the control target.

[0078] It is understandable that ​ exit ​ 、 ​ 3 Minimum concentration and control variables The goal is to minimize the rate of change and construct the objective function as follows:

[0079]

[0080] in:

[0081]

[0082] Where, x 1 representative ​ exit ​ concentration, x 2 representatives ​exit ​ 3 concentration; the first term of the objective function is expressed as ​ exit ​ The reference value of the concentration is 0, and the ​ The system control variable target ammonia storage capacity is ​ exit ​ Try to get closer to 0, the second term of the objective function is expressed ​ exit ​ 3The leakage volume is also infinitely close to 0. L 1. L 2. L 3 is the weight of the objective function, which indicates the effect of each item in the objective function. N p For the control domain and prediction domain of model predictive control, N p ≥1, generally set to 20.

[0083] The first term of the objective function represents ​ exit ​ The reference value of the concentration is 0, and the ​ The system control variable target ammonia storage capacity is ​ exit ​ Try to get closer to 0, the second term of the objective function is expressed ​ exit ​ 3The leakage volume is also infinitely close to 0.

[0084] L 1 is the value of the objective function ​ The weight, calibration value, also corresponds to the numerical value P 1;

[0085] L 2 is the objective function ​ The weight of 3, the calibration value, also corresponds to the numerical value P 2;

[0086] L 3 is the weight of ammonia coverage in the objective function, the calibration value, which also corresponds to the numerical value P 3;

[0087] ​ It is the sampling time or control period, which is generally 50ms;

[0088] U ( k )for k time ​ Ammonia coverage, U min is 0, U max is 1;

[0089] ​ For actual ​ Catalyst ammonia coverage; ​ max for ​ Maximum ammonia coverage of the system;

[0090] k 1 is the ammonia adsorption reaction rate constant;

[0091] k 2 is the ammonia desorption reaction rate constant;

[0092] k 3 for ​ Catalytic reduction reaction rate constant;

[0093]

[0094] is the airspeed, is the exhaust flow rate, V is the catalyst volume;

[0095] E 1 for ​ 3. Activation energy of adsorption reaction, which is a nominal value;

[0096] T for ​ Catalyst temperature, generally taken as ​ The average of the inlet and outlet temperatures.

[0097] In addition to considering ​ In addition to the system's emission requirements, it is also necessary to consider ​ The relevant constraints of the system mainly include the maximum injection capacity of the actuator urea pump, ​ The limit values ​​of each state quantity of the system. U min 、 U max for ​ Minimum and maximum limits for catalyst ammonia storage. U min Determined by working conditions, ​ The system always keeps a certain amount of ammonia storage to avoid ​ When the concentration changes rapidly, ​ exit ​ Surge in working conditions.

[0098] In summary, the model predictive control algorithm provided by the present invention mainly has the following three steps:

[0099] 1) At each sampling moment, based on the current measurement information (control sequence), the model of the controlled object is used to predict the future dynamics of the system;

[0100] 2) Online numerical solution to a finite-time open-loop optimization problem;

[0101] 3) Apply the first element of the obtained control sequence to the controlled object.

[0102] The repetition of these three steps constitutes the "rolling optimization" mechanism of model predictive control. In the control sequence initialization procedure, the control variable is assumed to remain constant within the prediction horizon. Within the constraints, the control variable is then assigned N equally spaced values. The corresponding objective function values ​​are then calculated based on the optimization problem described above.

[0103] In other embodiments, the present invention provides ​ The optimization control method of target ammonia storage can refer to ​ As shown, specifically including:

[0104] a) Determine the adaptive iteration step size:

[0105] In order to speed up the convergence process, the initial step size is set to be larger. As the iteration process progresses, the step size is gradually reduced and stabilized after N iterations.

[0106] b) State prediction:

[0107] According to the current control sequence and ​ The system dynamics model obtains the state quantity and objective function value in the prediction time domain.

[0108] c) Solve the calculation:

[0109] Calculate the gradient of the constraint function and solve it through gradient descent iterative optimization k The sequence of optimal control variables at time 0 [ u _ k 0, u _( k 0+1),⋯, u _( k 0+ ​ ) ], only take the first control quantity u _ k Act on the system. At the next moment k At time 0+1, repeat the above solution steps and get k The optimal control variable sequence at time 0+1[ u _( k 0+1),⋯, u _( k 0+ ​ +1)].

[0110] Control parameter design and optimization:

[0111] Three parameters of the objective function based on model predictive control L 1. L 2. L 3 have different physical meanings. L 1 is right ​ The emission limit weight increases L 1 will be right ​ The constraints are more stringent, making ​ Emission reduction and increase in urea injection; similarly increase L 2 will cause ​ 3. Reduction of escape volume and ​ Increased emissions; L 3 is a constraint item on the target ammonia storage capacity, which is a soft constraint on the target ammonia storage capacity.

[0112] At present, the control parameter calibration method is to coordinate control based on simulation test and bench calibration results, and the final engine emissions and urea consumption are evaluated as a whole.

[0113] In summary, the present invention provides ​ The optimization control method of the target ammonia storage capacity includes: obtaining the control sequence at the current moment; the control sequence is used to control ​ The ammonia storage capacity of the system; the control sequence based on the current moment and the preset ​ Dynamic model and corresponding objective function to determine the prediction time domain ​ The state quantity and control target of the system; based on the state quantity and the control target, determine the gradient of the control target to the control sequence at the current moment; based on the gradient and in combination with the gradient descent iterative method and the iteration step size, determine the optimal control sequence in the prediction time domain, and based on the optimal control sequence ​ The target ammonia storage capacity of the system is controlled.

[0114] The present invention provides ​ The optimization control method of the target ammonia storage capacity is based on the current ​ The control sequence of the system and the preset ​ Dynamic model and corresponding objective function to determine the prediction time domain ​ The state quantity and control target of the system, based on the state quantity and the control target, determine the gradient of the control target to the control sequence at the current moment; based on the gradient and combined with the gradient descent iterative method and iterative step size, determine the optimal control sequence in the prediction time domain, and realize the closed-loop optimization control strategy based on the model. ​ Kinetic models are predictive ​The future state quantity of the system is based on the gradient and combined with the gradient descent iterative method and iterative step size, and the control action and feedback correction of the model error are repeatedly optimized and calculated online and rolled. The future output of the controlled object is predicted by the model, and the behavior of the system is evaluated using the relevant objective function. The optimal control sequence in the future period of time is solved by online rolling optimization. The online rolling optimization provided by the present invention is used to solve the optimal control sequence. The model predictive control has the advantages of good control effect and strong robustness. It can effectively overcome the uncertainty, nonlinearity and parallelism of the process, improve the transient control effect, and optimize and upgrade the control function according to the actual working conditions, thereby improving ​ Ammonia storage control accuracy.

[0115] like ​ As shown, the present invention also provides a ​ The target ammonia storage optimization control device 400 includes:

[0116] Acquisition module 401 is used to obtain the control sequence at the current moment; the control sequence is used to control ​ Ammonia storage capacity of the system;

[0117] Prediction module 402, used for controlling sequence based on current moment and preset ​ Dynamic model and corresponding objective function to determine the prediction time domain ​ The state quantity and control target of the system;

[0118] A gradient solving module 403 is used to determine the gradient of the cost with respect to the control sequence at the current moment based on the state quantity and the control target;

[0119] The control module 404 is used to determine the optimal control sequence of the prediction time domain based on the gradient and in combination with the gradient descent iterative method and the iteration step size, and to ​ The target ammonia storage capacity of the system is controlled.

[0120] In some embodiments, the iteration step size is inversely correlated with the number of iterations of the gradient descent iterative method, and when the number of iterations is greater than a preset threshold, the iteration step size remains unchanged.

[0121] It is understandable that in order to accelerate the convergence process, the initial step size is set to be larger, and as the iteration process progresses, the step size is gradually reduced, and stabilizes at the step size after N iterations (i.e., the preset threshold).

[0122] In some embodiments, the ​ The state of the system includes: ​ System export ​ Concentration and ​ 3 concentration; ​ Kinetic models, including:​ Kinetic models and ​ 3. Kinetic model;

[0123] described ​ Dynamic model, used to characterize the current moment ​ System export ​ Concentration and last moment ​ System export ​ The relationship between concentrations;

[0124] described ​ 3 Dynamic model, used to characterize the current moment ​ System export ​ 3Concentration and last moment ​ System export ​ 3. The relationship between concentrations.

[0125] In some embodiments, the ​ The relationship represented by the kinetic model is:

[0126] Current moment ​ System export ​ Concentration is based on the previous moment ​ System export ​ Concentration, as well as control cycle, air speed, current time ​ System entrance ​ concentration, ​ Catalytic reduction reaction rate constant, ​ Activation energy of catalytic reduction reaction, ​ Catalyst temperature, last moment ​ The ammonia coverage of the system and ​ The maximum ammonia coverage of the system is determined.

[0127] In some embodiments, the ​ 3 The relationship represented by the kinetic model is:

[0128] Current moment ​ System export ​ 3 concentration, based on the last moment ​ System export ​ 3 concentration, as well as control cycle, air speed, current time ​ System inlet urea injection amount, ammonia desorption reaction rate constant, ammonia adsorption reaction rate constant, ​ 3Desorption reaction activation energy, last moment ​ Ammonia coverage of the system, ​ 3. Desorption reaction characteristic parameters, ​ The system's maximum ammonia coverage and ​ The catalyst temperature is determined.

[0129] In some embodiments, the objective function is based on the control period ​ System export ​ concentration, ​ 3. Determine the leakage amount and the rate of change of the control sequence.

[0130] In some embodiments, the objective function is to control the cycle ​ System export ​ concentration, ​ 3. The minimum weighted sum of leakage amount and control sequence change rate is the control target.

[0131] It is understandable that the present invention provides ​ The optimized control device for target ammonia storage capacity is ​ Kinetic model (i.e. ​ Model predictive control (MPC) uses a chemical reaction kinetics model to predict the future output of the controlled object, evaluates system behavior using correlation functions, and solves the optimal control sequence for a specific period of time. MPC offers advantages such as effective control and strong robustness. It effectively overcomes process uncertainty, nonlinearity, and parallelism, and can easily handle various constraints on the controlled and manipulated variables.

[0132] Specifically, the present invention provides ​ The optimization control device of the target ammonia storage amount is based on the current ​ The control sequence of the system and the preset ​ Dynamic model and corresponding objective function to determine the prediction time domain ​ The state quantity and control target of the system, based on the state quantity and the control target, determine the gradient of the control target to the control sequence at the current moment; based on the gradient and combined with the gradient descent iterative method and iterative step size, determine the optimal control sequence in the prediction time domain, and realize the closed-loop optimization control strategy based on the model. ​ Kinetic models are predictive ​ The future state quantity of the system is based on the gradient and combined with the gradient descent iterative method and iterative step size, and the control action and feedback correction of the model error are repeatedly optimized and calculated online and rolled. The future output of the controlled object is predicted by the model, and the behavior of the system is evaluated using the relevant objective function. The optimal control sequence in the future period of time is solved by online rolling optimization. The online rolling optimization provided by the present invention is used to solve the optimal control sequence. The model predictive control has the advantages of good control effect and strong robustness. It can effectively overcome the uncertainty, nonlinearity and parallelism of the process, improve the transient control effect, and optimize and upgrade the control function according to the actual working conditions, thereby improving ​ Ammonia storage control accuracy.

[0133] The above embodiments provide​ The optimized control device for target ammonia storage can achieve the above ​ The technical solution described in the embodiment of the optimization control method for target ammonia storage capacity, the specific implementation principles of the above modules or units can be found in the above ​ The corresponding contents in the embodiment of the optimization control method for the target ammonia storage amount will not be repeated here.

[0134] like ​ As shown, the present invention also provides an engine controller 500. The engine controller 500 includes a processor 501 and a memory 502. ​ Only some of the components of the engine controller 500 are shown, but it should be understood that implementing all of the shown components is not a requirement, and more or fewer components may alternatively be implemented.

[0135] The memory 502 may be an internal storage unit of the engine controller 500 in some embodiments.

[0136] In some embodiments, the processor 501 may be a central processing unit (CPU), a microprocessor or other data processing chip, which is used to run the program code stored in the memory 502 or process data, such as the CPU in the present invention. ​ Optimal control method for target ammonia storage capacity.

[0137] In some embodiments of the present invention, when executing the engine controller ​ When optimizing the control program for target ammonia storage, the following steps can be implemented:

[0138] Get the control sequence at the current moment; the control sequence is used to control ​ Ammonia storage capacity of the system;

[0139] Based on the control sequence at the current moment and the preset ​ Dynamic model and corresponding objective function to determine the prediction time domain ​ The state quantity and control target of the system;

[0140] Determining, based on the state quantity and the control target, a gradient of the control target with respect to the control sequence at a current moment;

[0141] Based on the gradient and in combination with the gradient descent iterative method and the iterative step size, the optimal control sequence of the prediction time domain is determined, and the optimal control sequence is used to determine the optimal control sequence of the prediction time domain. ​ The target ammonia storage capacity of the system is controlled.

[0142] It should be understood that: ​In addition to the above functions, other functions can also be realized during the optimization control program of the target ammonia storage amount. For details, please refer to the description of the corresponding method embodiment above.

[0143] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented by a processor to perform the above methods. ​ The optimization control method of the target ammonia storage amount comprises:

[0144] Get the control sequence at the current moment; the control sequence is used to control ​ Ammonia storage capacity of the system;

[0145] Based on the control sequence at the current moment and the preset ​ Dynamic model and corresponding objective function to determine the prediction time domain ​ The state quantity and control target of the system;

[0146] Determining, based on the state quantity and the control target, a gradient of the control target with respect to the control sequence at a current moment;

[0147] Based on the gradient and in combination with the gradient descent iterative method and the iterative step size, the optimal control sequence of the prediction time domain is determined, and the optimal control sequence is used to determine the optimal control sequence of the prediction time domain. ​ The target ammonia storage capacity of the system is controlled.

[0148] The present invention also provides a car, which includes the above-mentioned vehicle controller 500.

[0149] The above invention provides ​ The optimization control method, device and engine controller of the target ammonia storage amount are introduced in detail. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A SCR The optimization control method of target ammonia storage capacity is characterized by: include: Get the control sequence at the current moment; Control sequences are used to control SCR The ammonia storage capacity of the system; Based on the control sequence at the current moment, and the preset SCR Dynamic model and corresponding objective function to determine the prediction time domain SCR The state quantity and control target of the system; Based on the state quantity and the control target, determining the gradient of the control target to the control sequence at the current moment; Based on the gradient and in combination with the gradient descent iterative method and the iterative step size, an optimal control sequence in the prediction time domain is determined, and based on the optimal control sequence, SCR The target ammonia storage capacity of the system is controlled; The iteration step length is inversely correlated with the number of iterations of the gradient descent iterative method, and when the number of iterations is greater than a preset threshold, the iteration step length remains unchanged.

2. according to claim 1 SCR The optimization control method of target ammonia storage capacity is characterized by: Said SCR The state of the system includes: SCR System export NOx Concentration and NH 3 concentration; SCR Kinetic models, including: NOx Kinetic models and NH 3 Kinetic model; Said NOx Dynamic model, used to characterize the current moment SCR System export NOx Concentration and last moment SCR System export NOx The relationship between concentrations; Said NH 3 Kinetic model, used to characterize the current moment SCR System export NH 3 Concentration and last moment SCR System export NH 3. The relationship between concentrations.

3. According to claim 2 SCR The optimization control method of target ammonia storage capacity is characterized by: Said NOx The relationship represented by the kinetic model is: Current moment SCR System export NOx Concentration is based on the last moment SCR System export NOx Concentration, as well as control cycle, airspeed, current time SCR System entrance NOx concentration, NOx Catalytic reduction reaction rate constant, NOx Activation energy of catalytic reduction reaction, SCR Catalyst temperature, last moment SCR The ammonia coverage of the system and SCR The maximum ammonia coverage of the system is determined.

4. according to claim 3 SCR The optimization control method of target ammonia storage capacity is characterized by: Said NH 3 The relationship represented by the kinetic model is: Current moment SCR System export NH 3 concentration, based on the last moment SCR System export NH 3 concentration, as well as control cycle, air speed, current time SCR System inlet urea injection amount, ammonia desorption reaction rate constant, ammonia adsorption reaction rate constant, NH 3 Desorption reaction activation energy, last moment SCR Ammonia coverage of the system, NH 3. Desorption reaction characteristic parameters, SCR The maximum ammonia coverage of the system and SCR The catalyst temperature is determined.

5. According to claim 4 SCR The optimization control method of target ammonia storage capacity is characterized by: The objective function is based on the control cycle SCR System export NOx concentration, NH 3. Determine the leakage amount and the control sequence change rate.

6. According to claim 5 SCR The optimization control method of target ammonia storage capacity is characterized by: The objective function is to control the cycle SCR System export NOx concentration, NH 3 The minimum weighted sum of the leakage amount and the control sequence change rate is the control target.

7. A SCR The device for optimizing and controlling target ammonia storage capacity is characterized in that: include: An acquisition module is used to obtain the control sequence at the current moment; Control sequences are used to control SCR The ammonia storage capacity of the system; Prediction module, used for control sequence based on current moment and preset SCR Dynamic model and corresponding objective function to determine the prediction time domain SCR The state quantity and control target of the system; A gradient solving module, used for determining the gradient of the control target to the control sequence at the current moment based on the state quantity and the control target; A control module is used to determine the optimal control sequence of the prediction time domain based on the gradient and in combination with the gradient descent iterative method and the iteration step size, and to SCR The target ammonia storage capacity of the system is controlled; The iteration step length is inversely correlated with the number of iterations of the gradient descent iterative method, and when the number of iterations is greater than a preset threshold, the iteration step length remains unchanged.

8. An engine controller, characterized in that: comprising a memory and a processor, wherein: The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the method according to any one of claims 1 to 6. SCR Steps of the optimization control method for target ammonia storage.

9. A car, characterized in that: Includes the engine controller as claimed in claim 8.

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