A DPF overload judgment method, device, vehicle-mounted control system and automobile

Abstract

This invention provides a method, device, vehicle control system, and automobile for DPF overload judgment. By constructing and employing a Kalman filter algorithm to solve the relationship function between the pressure difference across the particulate filter and the exhaust gas volumetric flow rate, a target relationship function is obtained. After obtaining the measured quantities in the pressure difference and exhaust gas volumetric flow rate, these measured quantities are substituted into the target relationship function to calculate the synchronous theoretical non-measured quantity. Substituting these measured and theoretical non-measured quantities into the particulate filter control system, the control system can perform DPF analysis and control based on these quantities. This ensures the synchronization of the volumetric flow rate and pressure difference signals, guarantees the accuracy of DPF filtration efficiency monitoring and judgment, and avoids false alarms.

Description

Technical Field

[0001] This invention relates to the field of equipment monitoring technology, specifically to a DPF overload detection method, device, vehicle control system, and automobile. Background Technology

[0002] Particulate matter is one of the main pollutants in diesel engine exhaust emissions. Currently, the most effective aftertreatment device for reducing diesel engine particulate matter emissions is the wall-flow particulate filter (DPF). When a DPF captures particulate matter, its filtration efficiency needs to be monitored. During monitoring, it is necessary to determine the pressure difference at maximum volumetric flow rate or the volumetric flow rate at maximum pressure difference. To ensure the reliability of the analysis results, the pressure difference signal and volumetric flow rate signal in the analysis data must be kept synchronized. How to ensure that the pressure difference signal and volumetric flow rate signal used in the analysis are synchronized, and to achieve accurate judgment of DPF overload conditions to avoid false alarms, has become one of the technical problems that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a DPF overload judgment method, device, vehicle control system and automobile, so as to ensure that the differential pressure signal and volumetric flow rate signal used in the process of analyzing DPF are synchronous signals.

[0004] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0005] A DPF overload detection method includes:

[0006] Construct a relationship function between the pressure difference across the particulate matter collector and the volumetric flow rate of the exhaust gas, wherein the pressure difference and the volumetric flow rate of the exhaust gas in the relationship function are synchronous signals;

[0007] A recursive model is constructed using the Kalman filter algorithm, and the recursive model is used to solve the relational function to calculate the filtered values ​​of each coefficient in the relational function.

[0008] Substituting the filtered values ​​of each coefficient into the relation function yields the target relation function;

[0009] Acquire a measurement quantity, wherein the measurement quantity is one of the pressure difference and the exhaust gas volume flow rate;

[0010] The non-measurable quantities in the pressure difference and exhaust gas volume flow rate are obtained by back-calculating the obtained measured quantities using the target relation function, and are denoted as theoretical non-measurable quantities.

[0011] The measured quantities and theoretical non-measured quantities are substituted into the control system of the particulate matter trap.

[0012] Optionally, in the above DPF overload judgment method, the measured quantity is the exhaust gas volumetric flow rate;

[0013] The non-measurable quantity is the pressure difference across the particulate matter collector.

[0014] Optionally, in the above DPF overload judgment method, the relationship function between the pressure difference across the particulate matter collector and the exhaust gas volumetric flow rate is:

[0015] Δp=aQ 2 +bQ+cf(δ)+d;

[0016] Δp is the pressure difference across the particulate matter collector;

[0017] Q is the volumetric flow rate of the exhaust gas;

[0018] a, b, and c are the correlation characteristic coefficients;

[0019] The f(δ) represents random drift;

[0020] d represents the steady-state error of the measurement.

[0021] Optionally, in the above DPF overload judgment method, a Kalman filter algorithm is used to construct a recursive model, and the recursive model is used to solve the relational function to calculate the filtered values ​​of each coefficient in the relational function, including:

[0022] Construct matrix x = [abcd] T ;

[0023] Constructing state recurrence equations Recurrence equation for covariance

[0024] Construct the state transition matrix and Kalman gain matrix

[0025] Constructing the covariance update formula

[0026] Constructing the state update formula

[0027] Based on the aforementioned state recursive equation, covariance recursive equation, state transition matrix, Kalman gain matrix, covariance update formula, and state update formula, for x = [abcd]... T Solve the problem to obtain the specific values ​​of abcd;

[0028] X is a state variable;

[0029] Q1 represents the variance of the recursive model;

[0030] P is the covariance of the recursive model;

[0031] I is the identity matrix;

[0032] Indicates the volumetric flow rate of exhaust gas;

[0033] k is the Kalman gain coefficient;

[0034] z is the measured value from the differential pressure sensor;

[0035] H is the measurement matrix, used to represent the relationship between state variables and measured values;

[0036] The subscript k indicates the time.

[0037] Optionally, in the above DPF overload judgment method, after using the target relationship function to back-calculate the non-measurable quantities in the pressure difference and exhaust gas volumetric flow rate based on the obtained measured quantities, and recording them as theoretical non-measurable quantities, before substituting the measured quantities and theoretical non-measurable quantities into the particulate matter filter control system, the method further includes:

[0038] Calculate the inflection point time of the theoretical non-measurable quantity, and denote it as the first inflection point time;

[0039] Calculate the inflection point time of the measured quantity and record it as the second inflection point time;

[0040] Determine whether the difference between the first inflection point and the second inflection point is less than a preset duration;

[0041] If the difference is less than the preset duration, continue with the subsequent steps.

[0042] Optionally, in the above DPF overload judgment method, the Kalman filter algorithm is used to solve the relational function to calculate the filtered values ​​of each coefficient in the relational function, including:

[0043] The Kalman filter algorithm is used to solve the relation function to obtain the filtered values ​​of each coefficient in the relation function at each time point within a preset time period;

[0044] The average value of the filtered values ​​of each coefficient in the relational function corresponding to each moment within the preset time period is calculated to obtain the average value of the filtered values ​​of each coefficient in the relational function.

[0045] The average of the filtered values ​​of each coefficient is used as the filtered value of each coefficient in the final relational function.

[0046] Optionally, the above DPF overload judgment method also includes:

[0047] The measurement value of the non-measurable quantity obtained by the sensor at the first moment is recorded as the actual non-measurable quantity. The first moment is the acquisition moment used to back-calculate the measurement value of the non-measurable quantity.

[0048] Determine whether the difference between the actual non-measurable quantity and the theoretical non-measurable quantity is greater than a preset error value;

[0049] When the error exceeds the preset error value, a prompt signal is output to indicate a fault in the differential pressure sensor or the exhaust gas volume flow sensor.

[0050] A DPF overload detection device, comprising:

[0051] The parameter construction unit is used to construct the relationship function between the pressure difference across the particulate matter collector and the exhaust gas volume flow rate, wherein the pressure difference and exhaust gas volume flow rate in the relationship function are synchronization signals;

[0052] The Kalman processing unit is used to solve the relation function using the Kalman filtering algorithm, calculate the filtered value of each coefficient in the relation function, and substitute the filtered value of each coefficient into the relation function to obtain the target relation function.

[0053] A synchronization signal calculation unit is used to acquire a measured quantity, which is one of the pressure difference and the exhaust gas volume flow rate; the non-measured quantity between the pressure difference and the exhaust gas volume flow rate is calculated based on the acquired measured quantity using the target relationship function, and is denoted as the theoretical non-measured quantity; the measured quantity and the theoretical non-measured quantity are substituted into the control system of the particulate matter collector.

[0054] An onboard control system includes a memory and a processor;

[0055] The memory is used to store programs;

[0056] The processor is used to execute the program to implement each step of the DPF overload judgment method described above.

[0057] An automobile includes the vehicle control system described above.

[0058] Based on the above technical solution, the solution provided in this embodiment of the invention solves the relationship function between the pressure difference across the particulate matter filter and the volumetric flow rate of the exhaust gas by constructing and employing a Kalman filter algorithm. This yields a target relationship function. After obtaining the measured quantities in the pressure difference and exhaust gas volumetric flow rate, these measured quantities are substituted into the target relationship function to calculate the synchronous theoretical non-measured quantities. These measured quantities and theoretical non-measured quantities are then substituted into the particulate matter filter's control system. The control system can then perform DPF analysis and control based on these measured and theoretical non-measured quantities. This ensures the synchronization of the volumetric flow rate and pressure difference signals, guarantees the accuracy of DPF filtration efficiency monitoring and judgment, enables precise judgment of DPF overload conditions, and avoids false alarms. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0060] Figure 1 This is a flowchart illustrating a DPF overload determination method disclosed in an embodiment of this application.

[0061] Figure 2 This is a schematic flowchart illustrating the process of solving the target relation function using the Kalman filtering method disclosed in an embodiment of this application;

[0062] Figure 3 This is a flowchart illustrating a DPF overload determination method disclosed in another embodiment of this application;

[0063] Figure 4 This is a flowchart illustrating the process of solving the relational function in the DPF overload judgment method disclosed in this application.

[0064] Figure 5 This is a flowchart illustrating the process of determining whether a sensor is reliable in the DPF overload determination method disclosed in this application.

[0065] Figure 6 This is a schematic diagram of the simulation results of the DPF overload judgment method disclosed in the embodiments of this application;

[0066] Figure 7 This is a schematic diagram of the structure of the DPF overload detection device disclosed in the embodiments of this application;

[0067] Figure 8 This is a schematic diagram of the vehicle control system. Detailed Implementation

[0068] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0069] When the particulate matter captured by a DPF reaches a certain limit, measures must be taken to clean up the particulate matter, i.e., DPF regeneration. Currently, a solution to determine whether this limit has been reached is to install differential pressure sensors at both ends of the DPF. The differential pressure value measured by the sensor is used to determine whether the captured particulate matter has reached the limit. To determine whether the DPF has OBD (On-Board Diagnostics) faults such as aging, cracking, or removal, the differential pressure sensor is often used to measure the characteristics of the differential pressure value. The exhaust gas volumetric flow rate, which is strongly correlated with the differential pressure, is also used in this determination. Therefore, the two signals need to be synchronized and cannot have random delays or deviations.

[0070] The calculation process for exhaust gas volumetric flow rate is as follows:

[0071] Exhaust gas volumetric flow rate = exhaust gas mass flow rate / exhaust gas density;

[0072] Exhaust gas density is related to exhaust back pressure, which is mainly composed of atmospheric pressure + pipeline pressure drop + SCR (exhaust gas reduction catalytic converter) pressure drop + DPF pressure drop (DPF pressure difference). Due to differences in pipeline length and gas flow rate, there will be a signal delay in the measurement values ​​of exhaust gas volume flow rate and DPF pressure difference, which can be solved by Kalman filtering.

[0073] See Figure 1 The DPF overload determination method disclosed in this application may include:

[0074] Step S101: Construct the relationship function between the pressure difference across the particulate matter collector and the volumetric flow rate of the exhaust gas.

[0075] There is a certain mapping relationship between the pressure difference across the synchronous particulate matter collector and the exhaust gas volumetric flow rate. Given the exhaust gas volumetric flow rate, the corresponding pressure difference across the synchronous particulate matter collector can be calculated based on this mapping relationship. The reliability of the calculation results varies depending on the relational function. For example, the relational function described in this scheme is specifically:

[0076] Δp=aQ 2 +bQ+cf(δ)+d (Formula 1);

[0077] Δp is the pressure difference across the particulate matter collector;

[0078] Q is the volumetric flow rate of the exhaust gas;

[0079] a, b, and c are the correlation characteristic coefficients;

[0080] The f(δ) represents random drift;

[0081] d represents the steady-state error of the measurement.

[0082] In the initial relational function, a, b, c, and d are unknowns. After processing the relational function using the Kalman filter algorithm, values ​​can be assigned to a, b, c, and d in the relational function. This allows the pressure difference across the particulate matter collector to be directly calculated after substituting the exhaust gas volume flow rate into the assigned relational function.

[0083] Step S102: Use the Kalman filter algorithm to solve the relation function and calculate the filtered value of each coefficient in the relation function.

[0084] In this step, after constructing the relationship function between the pressure difference across the particulate matter collector and the volumetric flow rate of the exhaust gas, the weight coefficients (a, b, c, and d) of each function in the newly constructed relationship function are unknown. The Kalman filter algorithm is needed to solve the relationship function to obtain the filtered values ​​of the weight coefficients of each function in the relationship function. The filtered values ​​refer to the specific values ​​of the weight coefficients of each function.

[0085] Step S103: Substitute the filtered values ​​of each coefficient into the relation function to obtain the target relation function.

[0086] After calculating the filtered values ​​of each coefficient in the relational function based on the Kalman filter algorithm, substituting the filtered values ​​of each coefficient into the relational function yields a relational function containing only two unknowns. This relational function is denoted as the target relational function. One of these unknowns is the pressure difference across the particulate matter collector, and the other is the exhaust gas volumetric flow rate. These two unknowns are synchronously related. Given that the exhaust gas volumetric flow rate is known, the corresponding and synchronous pressure difference across the particulate matter collector can be calculated based on this target relational function.

[0087] Step S104: Obtain the measurement.

[0088] The measured quantity is either the differential pressure or the exhaust gas volumetric flow rate obtained by the sensor. The measurement value of the differential pressure sensor or the exhaust gas volumetric flow rate sensor, whichever has higher reliability, can be selected as the measured quantity. Here, the measured quantity refers to the measurement acquired by the sensor during actual application. During the design phase, it can be predefined whether to use the exhaust gas volumetric flow rate or the differential pressure as the measured quantity.

[0089] Step S105: Using the target relation function, the non-measurable quantities in the pressure difference and exhaust gas volume flow rate are calculated based on the obtained measured quantities and denoted as theoretical non-measurable quantities.

[0090] After constructing the objective function, the measured quantities collected during actual application are obtained. These measured quantities are then substituted into the objective relationship function for back-calculation, yielding the theoretical non-measured quantities synchronized with the measured quantities. The non-measured quantities are the pressure difference and exhaust gas volumetric flow rate, excluding the measured quantities.

[0091] When the measured quantity is the volumetric flow rate of the exhaust gas, the non-measured quantity is the pressure difference across the particulate matter collector.

[0092] When the measured quantity is the pressure difference across the particulate matter collector, the non-measured quantity is the volumetric flow rate of the exhaust gas.

[0093] Step S106: Substitute the measured quantity and the theoretical non-measured quantity into the control system of the particulate matter collector.

[0094] After determining the measured quantity and the theoretical non-measured quantity, the measured quantity and the theoretical non-measured quantity are substituted into the control system of the particulate matter filter. The control system can then perform DPF analysis and control and realize engine analysis and control based on the measured quantity and the theoretical non-measured quantity.

[0095] The above-described scheme disclosed in this application solves for the relationship function between the pressure difference across the particulate matter filter and the volumetric flow rate of the exhaust gas by constructing and employing a Kalman filter algorithm. A target relationship function is obtained. After acquiring the measured quantities in the pressure difference and exhaust gas volumetric flow rate, these measured quantities are substituted into the target relationship function to calculate the synchronous theoretical non-measured quantities. These measured quantities and theoretical non-measured quantities are then substituted into the particulate matter filter's control system. The control system can then perform DPF analysis and control based on these measured and theoretical non-measured quantities. This ensures the synchronization of the volumetric flow rate and pressure difference signals, guarantees the accuracy of DPF filtration efficiency monitoring and judgment, and avoids false alarms.

[0096] This embodiment discloses a method for establishing a recursive model using the Kalman filter principle, and using the recursive model to solve the relational function. For details, please refer to [link to relevant documentation]. Figure 2The method may include:

[0097] Step S201: Construct matrix x = [abcd] T .

[0098] Matrix x = [abcd] T In this context, abcd represents the relational function Δp = aQ mentioned above. 2 In the equation +bQ+cf(δ)+d, the coefficients abcd and x are defined as state variables.

[0099] Step S202: Construct the state recurrence equation Recurrence equation for covariance

[0100] P is the covariance of the recursive model;

[0101] Q1 represents the variance of the recursive model;

[0102] I is the identity matrix;

[0103] The This indicates a value that approximates the estimate of x at time k in the reverse direction; "^" represents the estimated value.

[0104] The This represents the value of the estimator of x at time k-1, which is positively approximating it.

[0105] The This represents the covariance of the recursive model approaching time k in reverse order;

[0106] The This represents the covariance of the recursive model that is positively approaching time k-1;

[0107] The subscript k indicates the k-th time.

[0108] Step S203: Construct the state transition matrix and Kalman gain matrix

[0109] ρ represents the density of the emitted exhaust gas;

[0110] The Indicates the volumetric flow rate of exhaust gas.

[0111] The μ = 0.000001;

[0112] H is a measurement matrix, which represents the relationship between state variables and measured values. The state variable is x mentioned earlier, and the measured value is z. k ;

[0113] R represents the sensor measurement variance, and here, the sensor refers to the exhaust gas volume flow sensor.

[0114] Step S204: Construct the covariance update formula

[0115] Step S205: Construct the state update formula

[0116] The z k This is the measured value from the differential pressure sensor. Step S206: Based on the state recursive equation, covariance recursive equation, state transition matrix, Kalman gain matrix, covariance update formula, and state update formula, for x = [ab cd]... T Solve to obtain the specific values ​​of abcd; after determining the state recursive equation, covariance recursive equation, state transition matrix, Kalman gain matrix, covariance update formula, and state update formula, apply these formulas to x = [ab cd]. T Solving for the specific value of abcd yields the solution. The specific solution process of the Kalman filter algorithm is an existing scheme and will not be elaborated here. After obtaining the specific value of abcd, substituting it into the relational function yields the target relational function. Based on the measurement quantity acquired through the corresponding sensor, substituting the measurement quantity into the target relational function yields the specific value of the non-measurement quantity synchronized with the measurement quantity.

[0117] The specific process of solving abcd can include:

[0118] Based on the state recurrence equation, let x k =Ax k-1 Wherein, A is a preset value.

[0119] Based on formula measurement equation make => ΔP = Hx k ;

[0120] During the iterative calculation, at the moment k = 0, let x k Equal to the preset standard quantities x0 and P k Equal to the preset standard quantity P0, substitute it into the formula Calculated and Then Assigned to x k-1 ,Will Assign to P k-1And then substitute it into the formula x k =Ax k-1 P k =AP k-1 A T +Q1, and we can calculate x at the next moment. k and P k After repeated iterations, until x k Convergence, x after convergence k This refers to the specific values ​​of the target coefficients in a, b, c, and d predicted in this study.

[0121] In the technical solution disclosed in this embodiment, to verify the reliability of the relationship function, this solution can also determine whether the measured and non-measured quantities are synchronization signals by calculating the inflection point times of the measured and non-measured quantities and comparing the difference between the inflection point times. For details, see [link to relevant documentation]. Figure 3 In this scheme, after using the target relationship function to back-calculate the non-measurable quantities in the pressure difference and exhaust gas volumetric flow rate based on the obtained measured quantities, and denoting them as theoretical non-measurable quantities, before substituting the measured quantities and theoretical non-measurable quantities into the control system of the particulate matter collector, the following steps are also included:

[0122] Step S301: Calculate the inflection point time of the theoretical non-measurable quantity, and record it as the first inflection point time.

[0123] When calculating the inflection point of the theoretical non-measurable quantity, the theoretical non-measurable quantity can be obtained, and the inflection point of the theoretical non-measurable quantity can be determined based on the derivative result. In this scheme, the inflection point refers to the peak value's ...

[0124] For example, when the theoretical non-measurable quantity is pressure difference, the inflection point of the pressure difference can be determined by taking the derivative of the pressure difference and the result of the derivative.

[0125] Step S302: Calculate the inflection point time of the measured quantity, and record it as the second inflection point time.

[0126] Similarly, by differentiating the measured quantity, the inflection point of the measured quantity can also be determined.

[0127] For example, when the measured quantity is the volumetric flow rate of exhaust gas, the inflection point of the volumetric flow rate of exhaust gas can be determined by differentiating the pressure difference and based on the derivative result.

[0128] Step S303: Determine whether the difference between the first inflection point time and the second inflection point time is less than a preset duration.

[0129] In this embodiment, a preset time period is established in advance. After determining the first inflection point time and the second inflection point time, the difference between the first inflection point time and the second inflection point time is calculated. It is determined whether the difference is less than the preset time period. If it is less than the preset time period, it indicates that the non-measured quantity calculated at this time is synchronized with the measured quantity. Otherwise, it indicates that the two are not synchronized. At this time, it is necessary to re-collect data for calculation or reconstruct the relation function, or use a recursive model to further solve the relation function. If the difference is less than the preset time period, it indicates that the non-measured quantity is synchronized with the measured quantity, and the calculation result of the target relation function is reliable. The subsequent steps are then executed.

[0130] In this embodiment, to ensure the reliability of the calculated filtered values, the Kalman filter algorithm is used to solve the relational function and calculate the filtered values ​​of each coefficient. This can be done based on continuous pressure difference and exhaust gas volumetric flow rate values ​​obtained over a period of time. This yields multiple sets of continuous abcd values. The average of these abcd values ​​is then calculated, and the average abcd value is used as the final abcd value, thereby improving the reliability of the calculated target relational function. For details, see [link to documentation]. Figure 4 This process may specifically include:

[0131] Step S401: Use the Kalman filter algorithm to solve the relation function to obtain the filtered values ​​of each coefficient in the relation function at each time point within the preset time period.

[0132] In this step, the Kalman filter algorithm is used to solve the problem based on the pressure difference and exhaust gas volume flow rate obtained within a preset time period. This allows us to obtain the filtered values ​​of each coefficient in the relational function at each moment within the preset time period. Of course, here, the pressure difference and exhaust gas volume flow rate are obtained under ideal conditions of synchronization.

[0133] Step S402: Calculate the mean of the filtered values ​​of each coefficient in the relational function corresponding to each moment within the preset time period to obtain the average value of the filtered values ​​of each coefficient in the relational function.

[0134] Step S403: The average value of the filtered values ​​of each coefficient is used as the filtered value of each coefficient in the final relational function.

[0135] For example, the calculated values ​​of 'a' can include (a1, a2, a3...an), the calculated values ​​of 'b' can include (b1, b2, b3...bn), the calculated values ​​of 'c' can include (c1, c2, c3...cn), and the calculated values ​​of 'd' can include (d1, d2, d3...dn). The average of a1, a2, a3...an is assigned to 'a', the average of b1, b2, b3...bn is assigned to 'b', the average of c1, c2, c3...cn is assigned to 'c', and the average of d1, d2, d3...dn is assigned to 'd'. Substituting these assigned values ​​(abcd) into the relational function yields the target relational function.

[0136] In this embodiment, in practical applications, the differential pressure sensor or exhaust gas volume flow sensor can also be judged to be faulty by comparing the theoretical non-measurable quantity obtained based on the extreme values ​​of the target relation function with the detection values ​​of the corresponding sensors. For details, see... Figure 5 This plan may also include:

[0137] Step S501: Obtain the measured value of the non-measured quantity obtained by the sensor at the first moment, and record it as the actual non-measured quantity.

[0138] The first time point is the time point at which the measured quantity is collected for back-calculating the non-measurable quantity. That is, the first time point is the time point at which the measured quantity is collected in "using the target relation function to back-calculate the non-measurable quantity in the pressure difference and exhaust gas volume flow rate based on the acquired measured quantity". For example, if the non-measurable quantity is back-calculated based on the measured quantity collected at time k using the target relation function, then time k is also the first time point.

[0139] Step S502: Determine whether the difference between the actual non-measurable quantity and the theoretical non-measurable quantity is greater than a preset error value.

[0140] In this embodiment, an error value is pre-marked. The difference between the actual non-measurable quantity and the theoretical non-measurable quantity is compared with the preset error value. Based on the comparison result, it can be determined whether the sensor is faulty. For example, if the difference is greater than the preset error value, it indicates that the differential pressure sensor or the exhaust gas volume flow sensor is faulty; if the difference is not greater than the preset error value, it indicates that the differential pressure sensor or the exhaust gas volume flow sensor is normal. Here, the theoretical non-measurable quantity refers to the non-measurable quantity calculated by substituting the measured quantity collected at the first moment into the target relational function.

[0141] Step S503: When the error exceeds the preset error value, output a prompt signal to indicate a fault in the differential pressure sensor or the exhaust gas volume flow sensor.

[0142] To further verify the effectiveness, the applicant also conducted simulation tests on this solution; the simulation results are available here. Figure 6 As shown, Figure 6 In the diagram, the bottom waveform is the waveform of the exhaust gas volume flow rate measured by the exhaust gas volume flow sensor, the top waveform is the waveform of the differential pressure measured by the differential pressure sensor, and the middle waveform is the waveform of the differential pressure calculated by back-calculating the exhaust gas volume flow rate measured by the exhaust gas volume flow sensor using the target relationship function. By comparing the top and middle waveforms, it is easy to see that the differential pressure calculated by back-calculating the exhaust gas volume flow rate measured by the exhaust gas volume flow sensor using the target relationship function has high reliability.

[0143] This embodiment also discloses a DPF overload detection device. For the specific working content of each unit in the device, please refer to the above method embodiment.

[0144] The DPF overload judgment device provided in the embodiments of the present invention is described below. The DPF overload judgment device described below and the DPF overload judgment method described above can be referred to in correspondence.

[0145] For details, see Figure 7 The device may include:

[0146] The parameter construction unit 10 is used to construct the relationship function between the pressure difference across the particulate matter collector and the exhaust gas volume flow rate, wherein the pressure difference and the exhaust gas volume flow rate in the relationship function are synchronization signals;

[0147] The Kalman processing unit 20 is used to solve the relation function using the Kalman filtering algorithm to calculate the filtered value of each coefficient in the relation function; and to substitute the filtered value of each coefficient into the relation function to obtain the target relation function.

[0148] The synchronization signal calculation unit 30 is used to acquire a measured quantity, which is one of the pressure difference and the exhaust gas volume flow rate; the non-measured quantity in the pressure difference and the exhaust gas volume flow rate is calculated based on the acquired measured quantity using the target relationship function, and is denoted as the theoretical non-measured quantity; the measured quantity and the theoretical non-measured quantity are substituted into the control system of the particulate matter collector.

[0149] Figure 8 The hardware structure diagram of the vehicle control system provided in the embodiment of the present invention is shown below. Figure 8 As shown, it may include: at least one processor 100, at least one communication interface 200, at least one memory 300 and at least one communication bus 400;

[0150] In this embodiment of the invention, the number of processor 100, communication interface 200, memory 300, and communication bus 400 is at least one, and the processor 100, communication interface 200, and memory 300 communicate with each other through communication bus 400; obviously, Figure 8 The communication connections shown for the processor 100, communication interface 200, memory 300, and communication bus 400 are optional.

[0151] Optionally, the communication interface 200 can be an interface of a communication module, such as the interface of a GSM module;

[0152] Processor 100 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention.

[0153] The memory 300 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0154] Specifically, processor 100 is used for:

[0155] Construct a relationship function between the pressure difference across the particulate matter collector and the volumetric flow rate of the exhaust gas, wherein the pressure difference and the volumetric flow rate of the exhaust gas in the relationship function are synchronous signals;

[0156] A recursive model is constructed using the Kalman filter algorithm, and the recursive model is used to solve the relational function to calculate the filtered values ​​of each coefficient in the relational function.

[0157] Substituting the filtered values ​​of each coefficient into the relation function yields the target relation function;

[0158] Acquire a measurement quantity, wherein the measurement quantity is one of the pressure difference and the exhaust gas volume flow rate;

[0159] The non-measurable quantities in the pressure difference and exhaust gas volume flow rate are obtained by back-calculating the obtained measured quantities using the target relation function, and are denoted as theoretical non-measurable quantities.

[0160] The measured quantities and theoretical non-measured quantities are substituted into the control system of the particulate matter trap.

[0161] In addition, this application also discloses a power device, which can be a car, and is equipped with the vehicle control system described in any of the above embodiments of this application.

[0162] For ease of description, the above system is described by dividing it into various modules based on their functions. Of course, in implementing this invention, the functions of each module can be implemented in one or more software and / or hardware components.

[0163] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the descriptions in the method embodiments. The systems and system embodiments described above are merely illustrative. 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 modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0164] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0165] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0166] It should also be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0167] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for determining DPF overload, characterized in that, include: Construct a relationship function between the pressure difference across the particulate matter collector and the volumetric flow rate of the exhaust gas, wherein the pressure difference and the volumetric flow rate of the exhaust gas in the relationship function are synchronous signals; A recursive model is constructed using the Kalman filter algorithm, and the recursive model is used to solve the relational function to calculate the filtered values ​​of each coefficient in the relational function. Substituting the filtered values ​​of each coefficient into the relation function yields the target relation function; Acquire a measurement quantity, wherein the measurement quantity is one of the pressure difference and the exhaust gas volume flow rate; The non-measurable quantities in the pressure difference and exhaust gas volume flow rate are obtained by back-calculating the obtained measured quantities using the target relation function, and are denoted as theoretical non-measurable quantities. Calculate the inflection point time of the theoretical non-measurable quantity, and denote it as the first inflection point time; Calculate the inflection point time of the measured quantity and record it as the second inflection point time; Determine whether the difference between the first inflection point and the second inflection point is less than a preset duration; If the difference is less than the preset duration, continue with the subsequent steps; otherwise, recollect data for calculation or rebuild the relational function, or use a recursive model to further solve the relational function until the difference is less than the preset duration. The measured quantities and theoretical non-measured quantities are substituted into the control system of the particulate matter trap, and the control system performs DPF analysis and control based on the measured quantities and theoretical non-measured quantities.

2. The DPF overload judgment method according to claim 1, characterized in that, The measured quantity is the volumetric flow rate of the exhaust gas; The non-measurable quantity is the pressure difference across the particulate matter collector.

3. The DPF overload judgment method according to claim 2, characterized in that, The relationship between the pressure difference across the particulate matter filter and the volumetric flow rate of the exhaust gas is as follows: ； The The pressure difference across the particulate matter collector; Q is the volumetric flow rate of the exhaust gas; a, b, and c are the correlation characteristic coefficients; The It is a random drift; d represents the steady-state error of the measurement.

4. The DPF overload judgment method according to claim 3, characterized in that, A recursive model is constructed using the Kalman filter algorithm. This recursive model is then used to solve the relational function to obtain the filtered values ​​of each coefficient in the relational function, including: Constructing a matrix ; Constructing state recurrence equations Recurrence equation for covariance ; Construct the state transition matrix and Kalman gain matrix ; Constructing the covariance update formula ; Constructing the state update formula ; Based on the aforementioned state recursive equation, covariance recursive equation, state transition matrix, Kalman gain matrix, covariance update formula, and state update formula, for the... Solve the problem to obtain the specific values ​​of abcd; X is a state variable; The variance of the recursive model; P is the covariance of the recursive model; I is the identity matrix; Indicates the volumetric flow rate of exhaust gas; k is the Kalman gain coefficient; z is the measured value from the differential pressure sensor; H is the measurement matrix, used to represent the relationship between state variables and measured values; The subscript k indicates the time.

5. The DPF overload judgment method according to claim 1, characterized in that, The Kalman filter algorithm is used to solve the relation function, and the filtered values ​​of each coefficient in the relation function are calculated, including: The Kalman filter algorithm is used to solve the relation function to obtain the filtered values ​​of each coefficient in the relation function at each time point within a preset time period; The average value of the filtered values ​​of each coefficient in the relational function corresponding to each moment within the preset time period is calculated to obtain the average value of the filtered values ​​of each coefficient in the relational function. The average of the filtered values ​​of each coefficient is used as the filtered value of each coefficient in the final relational function.

6. The DPF overload judgment method according to claim 1, characterized in that, Also includes: The measurement value of the non-measurable quantity obtained by the sensor at the first moment is recorded as the actual non-measurable quantity. The first moment is the acquisition moment used to back-calculate the measurement value of the non-measurable quantity. Determine whether the difference between the actual non-measurable quantity and the theoretical non-measurable quantity is greater than a preset error value; When the error exceeds the preset error value, a prompt signal is output to indicate a fault in the differential pressure sensor or the exhaust gas volume flow sensor.

7. A DPF overload detection device, used to execute the DPF overload detection method according to any one of claims 1-6, characterized in that, The device includes: The parameter construction unit is used to construct the relationship function between the pressure difference across the particulate matter collector and the exhaust gas volume flow rate, wherein the pressure difference and exhaust gas volume flow rate in the relationship function are synchronization signals; The Kalman processing unit is used to solve the relation function using the Kalman filtering algorithm, calculate the filtered value of each coefficient in the relation function, and substitute the filtered value of each coefficient into the relation function to obtain the target relation function. A synchronization signal calculation unit is used to acquire a measured quantity, which is one of the pressure difference and the exhaust gas volume flow rate; the non-measured quantity in the pressure difference and the exhaust gas volume flow rate is calculated based on the acquired measured quantity using the target relationship function, and is denoted as the theoretical non-measured quantity; the measured quantity and the theoretical non-measured quantity are substituted into the control system of the particulate matter filter, and the control system performs DPF analysis and control based on the measured quantity and the theoretical non-measured quantity.

8. A vehicle-mounted control system, characterized in that, Including memory and processor; The memory is used to store programs; The processor is used to execute the program to implement each step of the DPF overload judgment method as described in any one of claims 1-6.

9. A car, characterized in that, Includes the vehicle control system as described in claim 8.

Images