Oxygen sensor diagnosis method, device, equipment, storage medium and program product

Abstract

The invention discloses an oxygen sensor diagnosis method, device and equipment, a storage medium and a program product, and belongs to the technical field of vehicles. The method comprises the steps that under the condition that the diagnosis activation condition of the oxygen sensor is met, the actual combustion state of the engine is determined according to the measurement Lambda of the oxygen sensor; executing a diagnosis task of the oxygen sensor according to the actual combustion state; for each single-side degradation diagnosis, acquiring a delay time integral and a climbing time integral in the process of changing the measurement Lambda of the oxygen sensor to a target Lambda corresponding to the single-side degradation diagnosis; according to the delay time integral and the climbing time integral of each thin-to-thick single-side degradation diagnosis in the diagnosis task, determining a thin-to-thick single-side degradation diagnosis result of the oxygen sensor; and according to the delay time integral and the climbing time integral of each thick-to-thin single-side degradation diagnosis in the diagnosis task, determining a thick-to-thin single-side degradation diagnosis result of the oxygen sensor. The diagnosis reliability of the oxygen sensor can be improved.

Description

Technical Field

[0001] The present application belongs to the field of vehicle technology, and in particular, relates to an oxygen sensor diagnosis method, device, equipment, storage medium and program product. Background Art

[0002] In the electronic control system of gasoline engines, the oxygen sensor plays the role of Lambda closed-loop control and regulating the fuel supply. At the same time, the diagnosis of catalyst degradation also depends on the oxygen sensor. Therefore, as a component that is required to be tested by regulations, the diagnosis of the oxygen sensor is particularly important. The diagnosis of the front oxygen sensor is to determine whether the performance of the oxygen sensor has declined, which affects the control accuracy of Lambda and causes emission deterioration.

[0003] Currently, the diagnosis of oxygen sensors usually adopts a bilateral diagnosis method, which cannot realize the unilateral degradation diagnosis of oxygen sensors, resulting in low diagnostic reliability of oxygen sensors. Summary of the invention

[0004] The embodiments of the present application provide an oxygen sensor diagnosis method, device, equipment, storage medium and program product, which can solve the problem that the existing oxygen sensor diagnosis method cannot realize unilateral degradation diagnosis of the oxygen sensor, resulting in low diagnostic reliability of the oxygen sensor.

[0005] In a first aspect, an embodiment of the present application provides an oxygen sensor diagnosis method, the method comprising:

[0006] When the diagnostic activation condition of the oxygen sensor is met, determining the actual combustion state of the engine according to the measured Lambda of the oxygen sensor;

[0007] When the actual combustion state is a lean state or a rich state, the diagnostic task of the oxygen sensor is performed; wherein the diagnostic task includes N diagnostic cycles, each of which includes a lean-to-rich unilateral degradation diagnosis and a rich-to-lean unilateral degradation diagnosis, and N is a positive integer;

[0008] For each of the unilateral degradation diagnosis, a delay time integral and a climbing time integral are obtained in a process in which the measured Lambda of the oxygen sensor changes to the target Lambda corresponding to the unilateral degradation diagnosis; wherein the delay time integral is used to characterize the response delay of the oxygen sensor, and the climbing time integral is used to characterize the climbing rate of the oxygen sensor;

[0009] determining a lean-to-rich unilateral degradation diagnosis result of the oxygen sensor according to the delay time integral and the ramp-up time integral of each of the lean-to-rich unilateral degradation diagnosis in the diagnostic task;

[0010] A rich-to-lean unilateral degradation diagnosis result of the oxygen sensor is determined based on the delay time integral and the climb time integral of each of the rich-to-lean unilateral degradation diagnosis in the diagnostic task.

[0011] In a second aspect, an embodiment of the present application provides an oxygen sensor diagnostic device, the device comprising:

[0012] a first determination module, configured to determine an actual combustion state of the engine according to Lambda measured by the oxygen sensor when a diagnosis activation condition of the oxygen sensor is met;

[0013] an execution module, configured to execute a diagnostic task of the oxygen sensor when the actual combustion state is a lean state or a rich state; wherein the diagnostic task includes N diagnostic cycles, each of which includes a lean-to-rich unilateral degradation diagnosis and a rich-to-lean unilateral degradation diagnosis, and N is a positive integer;

[0014] an acquisition module, configured to acquire, for each of the unilateral degradation diagnosis, a delay time integral and a climb time integral in a process in which the measured Lambda of the oxygen sensor changes to the target Lambda corresponding to the unilateral degradation diagnosis; wherein the delay time integral is used to characterize the response delay of the oxygen sensor, and the climb time integral is used to characterize the climb rate of the oxygen sensor;

[0015] a second determination module, configured to determine a lean-to-rich unilateral degradation diagnosis result of the oxygen sensor according to the delay time integral and the climb time integral of each of the lean-to-rich unilateral degradation diagnosis in the diagnosis task;

[0016] The third determination module is configured to determine a rich-to-lean unilateral degradation diagnosis result of the oxygen sensor according to the delay time integral and the climb time integral of each of the rich-to-lean unilateral degradation diagnosis in the diagnosis task.

[0017] In a third aspect, an embodiment of the present application provides an oxygen sensor diagnostic device, the device comprising: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the oxygen sensor diagnostic method as described in the first aspect is implemented.

[0018] In a fourth aspect, an embodiment of the present application provides a computer storage medium, wherein the computer readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the oxygen sensor diagnostic method as described in the first aspect is implemented.

[0019] In a fifth aspect, an embodiment of the present application provides a computer program product. When instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the oxygen sensor diagnostic method as described in the first aspect.

[0020] In the embodiment of the present application, when the diagnostic activation conditions of the oxygen sensor are met, the oxygen sensor can be actively diagnosed. Specifically, the actual combustion state of the engine can be determined according to the measured Lambda of the oxygen sensor, and then the diagnostic task of the oxygen sensor is performed according to the actual combustion state of the engine. For each unilateral degradation diagnosis in the diagnostic task, the delay time integral and the climbing time integral in the process of the measured Lambda of the oxygen sensor changing to the target Lambda corresponding to the unilateral degradation diagnosis are obtained. Then, according to the delay time integral and the climbing time integral of each lean-to-rich unilateral degradation diagnosis in the diagnostic task, the unilateral degradation diagnosis result of the oxygen sensor from lean to rich is determined; according to the delay time integral and the climbing time integral of each lean-to-rich unilateral degradation diagnosis in the diagnostic task, the unilateral degradation diagnosis result of the oxygen sensor from lean to rich is determined. It can be seen that in the process of diagnosing the oxygen sensor, the embodiment of the present application can not only realize the unilateral degradation diagnosis of the oxygen sensor from lean to rich, but also the unilateral degradation diagnosis of rich to lean, thereby improving the diagnostic reliability of the oxygen sensor. In the unilateral degradation diagnosis, the response phase and the climbing phase of the oxygen sensor are further distinguished, and the delay time integral of the oxygen sensor in the response phase and the climbing time integral of the oxygen sensor in the climbing phase are obtained, wherein the delay time integral can characterize the response delay of the oxygen sensor, and the climbing time integral can characterize the climbing rate of the oxygen sensor. In this way, the lean-to-rich unilateral degradation diagnosis result of the oxygen sensor is determined by the delay time integral and the climbing time integral of each lean-to-rich unilateral degradation diagnosis in the diagnostic task, and the rich-to-lean unilateral degradation diagnosis result of the oxygen sensor is determined by the delay time integral and the climbing time integral of each rich-to-lean unilateral degradation diagnosis in the diagnostic task, thereby further improving the diagnostic reliability of the oxygen sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0022] Figure 1 is a schematic diagram of an oxygen sensor provided in an embodiment of the present application;

[0023] Figure 2 It is one of the flow charts of the oxygen sensor diagnostic method provided in the embodiment of the present application;

[0024] Figure 3It is one of the schematic diagrams of unilateral degradation diagnosis provided in the embodiment of the present application;

[0025] Figure 4 This is the second schematic diagram of unilateral degradation diagnosis provided in the embodiment of the present application;

[0026] Figure 5 This is the second flow chart of the oxygen sensor diagnostic method provided in the embodiment of the present application;

[0027] Figure 6 is a structural diagram of an oxygen sensor diagnostic device provided in an embodiment of the present application;

[0028] Figure 7 is a structural diagram of an oxygen sensor diagnostic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0030] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0031] For ease of understanding, some contents involved in the embodiments of the present application are described below:

[0032] At present, in response to the country's "going global" policy, China's automobile industry is actively improving various automobile performance indicators while also needing to meet overseas emission diagnostic regulations. However, countries around the world have different requirements for emission diagnostic regulations.

[0033] At present, the domestic diagnosis method for oxygen sensor response degradation is bilateral diagnosis, which is not applicable to emission diagnosis regulations that require the distinction between lean to rich and rich to lean unilateral degradation diagnosis. Under emission diagnosis regulations that require the distinction between lean to rich and rich to lean unilateral degradation diagnosis, if bilateral diagnosis is still used, the reliability of oxygen sensor diagnosis will be low, which will lead to vehicle emissions not meeting emission standards.

[0034] Based on this, the embodiments of the present application provide an oxygen sensor diagnosis method, device, equipment, storage medium and program product, which can not only realize the unilateral degradation diagnosis from lean to rich and the unilateral degradation diagnosis from rich to lean of the oxygen sensor, but also distinguish the response phase and the climbing phase of the oxygen sensor, and use the delay time integral of the oxygen sensor in the response phase and the climbing time integral of the oxygen sensor in the climbing phase to determine the unilateral degradation diagnosis result of the oxygen sensor, so that the diagnosis of the oxygen sensor complies with the corresponding emission diagnosis regulations.

[0035] The diagnosis of the oxygen sensor in the embodiment of the present application meets the frequency of the operation of the diagnostic function (In-Use Performance Ratio, IUPR) and the emission limit value during the daily use of the vehicle.

[0036] The present application does not limit the type and structure of the oxygen sensor. In some embodiments, the oxygen sensor can be a front oxygen sensor, a wide-range oxygen sensor, or a rear oxygen sensor. In some embodiments, the structure of the oxygen sensor can be referred to in Figure 1 , but it is not limited to this.

[0037] The oxygen sensor is used in the emission control system to maintain the actual air-fuel ratio of the engine at the theoretical air-fuel ratio. The lambda (λ) measured by the oxygen sensor can be understood as the ratio of the actual air-fuel ratio measured by the oxygen sensor to the theoretical air-fuel ratio.

[0038] The oxygen sensor diagnostic method provided by the embodiment of the present application is described in detail below through some embodiments and their application scenarios in combination with the accompanying drawings.

[0039] The oxygen sensor diagnostic method of the embodiment of the present application can be applied to a vehicle. Figure 2 , Figure 2 This is one of the flow charts of the oxygen sensor diagnostic method provided in the embodiment of the present application. Figure 2 As shown, the oxygen sensor diagnostic method may include the following steps:

[0040] Step 201: When the diagnostic activation condition of the oxygen sensor is met, determine the actual combustion state of the engine according to the measured Lambda of the oxygen sensor.

[0041] In the embodiment of the present application, the diagnosis activation condition of the oxygen sensor can be pre-set. When this condition is met, the oxygen sensor can be actively diagnosed to improve the autonomy of the oxygen sensor diagnosis.

[0042] In step 201, the diagnostic activation condition of the oxygen sensor is satisfied, so the active diagnosis of the oxygen sensor can be activated, and the actual combustion state of the engine can be determined by using the measured Lambda of the oxygen sensor. In the embodiment of the present application, the combustion state of the engine can be a lean state or a rich state, the lean state can also be called a lean end, and the rich state can also be called a rich end.

[0043] In some embodiments, a critical Lambda for distinguishing between a lean state and a rich state may be preset, hereinafter referred to as a preset Lambda. In specific implementation, the actual combustion state of the engine may be determined by comparing the Lambda measured by the oxygen sensor with the preset Lambda. If the Lambda measured by the oxygen sensor is greater than or equal to the preset Lambda, it may be determined that the engine is at the lean end, that is, the actual combustion state of the engine is a lean state; otherwise, it may be determined that the engine is at the rich end, that is, the actual combustion state of the engine is a rich state.

[0044] In some other embodiments, determining the actual combustion state of the engine according to the measured Lambda of the oxygen sensor may include:

[0045] Determining an initial combustion state of the engine according to a first comparison result of the measured Lambda of the oxygen sensor and the preset Lambda; wherein, when the measured Lambda of the oxygen sensor is greater than or equal to the preset Lambda, the initial combustion state is a lean state; and when the measured Lambda of the oxygen sensor is less than the preset Lambda, the initial combustion state is a rich state;

[0046] When the measured Lambda of the oxygen sensor meets the target condition, or the time when the measured Lambda of the oxygen sensor does not meet the target condition is greater than a preset time, the initial combustion state is determined as the actual combustion state of the engine; wherein the target condition is that the deviation between the measured Lambda of the oxygen sensor and the reference Lambda corresponding to the initial combustion state is less than a second deviation threshold; and the reference Lambda corresponding to the lean state is greater than the reference Lambda corresponding to the rich state.

[0047] In this embodiment, considering the signal delay between the engine and the oxygen sensor, the Lambda measured by the oxygen sensor at the current moment reflects the combustion state of the engine at the previous moments. Therefore, in these embodiments, the actual combustion state of the engine can be estimated by comparing the Lambda measured by the oxygen sensor with the preset Lambda to obtain the initial combustion state of the engine. In addition, for different combustion states, a corresponding reference Lambda can be set respectively, and then based on the Lambda measured by the oxygen sensor and the reference Lambda corresponding to the estimated initial combustion state of the engine, it is determined whether the combustion state of the engine is stable, thereby determining whether the above-mentioned estimation result is accurate, and then determining the actual combustion state of the engine.

[0048] In specific implementation, the deviation between the measured Lambda of the oxygen sensor and the reference Lambda may be calculated, and then the deviation may be compared with a second deviation threshold to determine whether the above evaluation result is accurate.

[0049] If this deviation is less than the second deviation threshold, that is, the measured Lambda of the oxygen sensor meets the target condition, it means that the combustion state of the engine is stable, the above evaluation result is accurate, and the previously determined initial combustion state can be determined as the actual combustion state of the engine.

[0050] If this deviation is greater than or equal to the second deviation threshold, that is, the measured Lambda of the oxygen sensor does not meet the target condition, it means that the combustion state of the engine is not stable, and the measured Lambda of the oxygen sensor can be continuously monitored until the measured Lambda of the oxygen sensor meets the target condition, or the measured Lambda of the oxygen sensor does not meet the target condition for a time greater than a preset time. If the measured Lambda of the oxygen sensor does not meet the target condition for a time greater than a preset time, it can be identified as Lambda jump stuck, and the oxygen sensor can be forced to be diagnosed, and the previously determined initial combustion state is determined as the actual combustion state of the engine, so as to perform the diagnostic task of the oxygen sensor according to this combustion state, so that the autonomy of the oxygen sensor diagnosis can be improved.

[0051] The actual combustion state of the engine determined by this embodiment takes into account the signal delay between the engine and the oxygen sensor, so that the accuracy of determining the actual combustion state of the engine can be improved, thereby improving the diagnostic accuracy of the oxygen sensor.

[0052] Step 202, executing the diagnostic task of the oxygen sensor according to the actual combustion state; wherein the diagnostic task includes N diagnostic cycles, each of which includes a lean-to-rich unilateral degradation diagnosis and a rich-to-lean unilateral degradation diagnosis, and N is a positive integer; when the actual combustion state is a lean state, the first unilateral degradation diagnosis of the diagnostic task is the lean-to-rich unilateral degradation diagnosis, and when the actual combustion state is a rich state, the first unilateral degradation diagnosis of the diagnostic task is the rich-to-lean unilateral degradation diagnosis.

[0053] After determining the actual combustion state of the engine, the oxygen sensor diagnosis task can be started according to the actual combustion state of the engine.

[0054] In the embodiment of the present application, the diagnostic task of the oxygen sensor may include N diagnostic cycles, each diagnostic cycle includes a lean to rich unilateral degradation diagnosis, and a rich to lean unilateral degradation diagnosis. It can be understood that the types of two adjacent unilateral degradation diagnoses in the diagnostic task are different. In this way, by executing the diagnostic task of the oxygen sensor in the embodiment of the present application, the unilateral degradation diagnosis of the oxygen sensor can be implemented.

[0055] N is a positive integer and can be set according to actual needs. In some embodiments, N can be 3, that is, the diagnostic task of the oxygen sensor can include 3 diagnostic cycles. In this way, the resource occupancy of the oxygen sensor diagnosis can be reduced on the basis of improving the reliability of the oxygen sensor diagnosis.

[0056] The actual combustion state of the engine determined in step 201 can be used to determine the type of the first unilateral degradation diagnosis of the diagnostic task of the oxygen sensor. Specifically, if the actual combustion state of the engine is a lean state, the first unilateral degradation diagnosis of the diagnostic task of the oxygen sensor is a lean-to-rich unilateral degradation diagnosis; if the actual combustion state of the engine is a rich state, the first unilateral degradation diagnosis of the diagnostic task of the oxygen sensor is a rich-to-lean unilateral degradation diagnosis.

[0057] Step 203: For each of the unilateral degradation diagnosis, obtain the delay time integral and the climb time integral in the process of the measured Lambda of the oxygen sensor changing to the target Lambda corresponding to the unilateral degradation diagnosis; wherein the delay time integral is used to characterize the response delay of the oxygen sensor, and the climb time integral is used to characterize the climb rate of the oxygen sensor.

[0058] In the embodiment of the present application, a target Lambda can be determined for each unilateral degradation diagnosis. The target Lambda can be understood as the Lambda that is expected to be achieved. Therefore, in each unilateral degradation diagnosis process, the measured Lambda of the oxygen sensor will change to the target Lambda corresponding to the unilateral degradation diagnosis. The time when the measured Lambda of the oxygen sensor changes to the target Lambda corresponding to the unilateral degradation diagnosis can be called a time integration window or a response time window.

[0059] In specific implementation, when performing a unilateral degradation diagnosis, the target Lambda corresponding to the unilateral degradation diagnosis can be transmitted to the engine so that the engine adjusts the fuel supply to change the air-fuel ratio, thereby changing the measured Lambda of the oxygen sensor to the target Lambda corresponding to the unilateral degradation diagnosis.

[0060] The target Lambda corresponding to the lean-to-rich unilateral degradation diagnosis is smaller than the target Lambda corresponding to the rich-to-lean unilateral degradation diagnosis. The value of each unilateral degradation diagnosis target Lambda can be set based on actual needs. In some embodiments, for the same type of unilateral degradation diagnosis in the diagnostic task, the corresponding target Lambda can be the same. In one example, the target Lambda corresponding to the lean-to-rich unilateral degradation diagnosis can be 0.97, and the target Lambda corresponding to the rich-to-lean unilateral degradation diagnosis can be 1.03, but it is not limited thereto.

[0061] It is worth noting that in the embodiment of the present application, in the unilateral degradation diagnosis, the response (delay) phase and the slope (slope) phase of the oxygen sensor are further distinguished. In the time integration window of the unilateral degradation diagnosis, the time integral of the oxygen sensor in the delay phase (i.e., the delay time integral) and the time integral of the oxygen sensor in the slope phase (i.e., the slope time integral) are obtained respectively.

[0062] The time integral of the oxygen sensor in the delay phase can characterize the response delay of the oxygen sensor, and the time integral of the oxygen sensor in the slope phase can characterize the climbing rate of the oxygen sensor. In this way, the response rate of the oxygen sensor can be evaluated by the time integral of the oxygen sensor in the delay phase, and the climbing rate of the oxygen sensor can be evaluated by the time integral of the oxygen sensor in the slope phase. In this way, the unilateral degradation diagnosis result can be determined by the delay time integral and the climbing time integral of the unilateral degradation diagnosis, which can improve the accuracy of the unilateral degradation diagnosis of the oxygen sensor.

[0063] Normally, if Figure 3As shown, during the unilateral degradation diagnosis process, the measured Lambda of the oxygen sensor will change according to a specific change trend (signal fluctuations within a small range are allowed) to the target Lambda, and there will be no signal oscillation. Based on this, in some embodiments, the change characteristics of the Lambda corresponding to the unilateral degradation diagnosis can be further identified to determine whether to select the time integration window of this unilateral degradation diagnosis for the unilateral degradation diagnosis of the oxygen sensor. In specific implementation, if there is no signal oscillation in the Lambda corresponding to the unilateral degradation diagnosis, it means that everything is normal in the unilateral degradation diagnosis process, and it can be regarded as a valid unilateral degradation diagnosis. The time integration window corresponding to the unilateral degradation diagnosis can be selected, and then the delay time integral and the climbing time integral corresponding to the unilateral degradation diagnosis can be obtained in this time integration window for the unilateral degradation diagnosis of the oxygen sensor; if there is signal oscillation in the Lambda corresponding to the unilateral degradation diagnosis, it means that an abnormality occurs, such as possible emission abnormality, and the unilateral degradation diagnosis can be regarded as an invalid unilateral degradation diagnosis, and the time integration window corresponding to the unilateral degradation diagnosis is abandoned for the unilateral degradation diagnosis of the oxygen sensor. In this embodiment, for effective unilateral degradation diagnosis, step 203 is executed, and for invalid unilateral degradation diagnosis, step 203 is not executed. In this way, by identifying the signal oscillation during the rise and fall of Lambda and selecting a reasonable time integration window, it is possible to maintain good robustness to emissions while improving diagnostic reliability.

[0064] Step 204: Determine a lean-to-rich unilateral degradation diagnosis result of the oxygen sensor according to the delay time integral and the climb time integral of each lean-to-rich unilateral degradation diagnosis in the diagnostic task.

[0065] In some embodiments, for the lean-to-rich unilateral degradation diagnosis, a time threshold may be set for the delay phase, recorded as the first time threshold; and a time threshold may be set for the slope phase, recorded as the second time threshold.

[0066] In specific implementation, the first target delay time integral can be determined based on the delay time integrals of all lean-to-rich unilateral degradation diagnoses obtained in the diagnostic task, and the first target climb time integral can be determined based on the climb time integrals of all lean-to-rich unilateral degradation diagnoses obtained in the diagnostic task. For example, the median or average value of the delay time integrals of all lean-to-rich unilateral degradation diagnoses can be determined as the first target delay time integral. The median or average value of the climb time integrals of all lean-to-rich unilateral degradation diagnoses can be determined as the first target climb time integral.

[0067] Then, the first target delay time integral is compared with a first time threshold, and the first target climb time integral is compared with a second time threshold.

[0068] If the first target delay time integral is greater than or equal to the first time threshold, it means that the response delay of the oxygen sensor does not meet the requirement, and the lean-to-rich unilateral degradation diagnosis result of the oxygen sensor can be determined as a lean-to-rich response degradation fault. Furthermore, it can be further indicated that the fault cause of the lean-to-rich response degradation fault is that the response delay of the oxygen sensor does not meet the requirement.

[0069] If the first target climb time integral is greater than or equal to the second time threshold, it means that the climb rate of the oxygen sensor does not meet the requirement, and the lean-to-rich unilateral degradation diagnosis result of the oxygen sensor can be determined as a lean-to-rich response degradation fault. Furthermore, it can be further indicated that the climb rate of the lean-to-rich response degradation fault is that the response delay of the oxygen sensor does not meet the requirement.

[0070] If the first target delay time integral is less than the first time threshold, and the first target climb time integral is less than the second time threshold, it means that the response delay and climb rate of the oxygen sensor meet the requirements, and the lean-to-rich unilateral degradation diagnosis result of the oxygen sensor can be determined as a normal lean-to-rich response.

[0071] In this embodiment, by separately analyzing the delay time integral and the climbing time integral of the lean-to-rich unilateral degradation diagnosis, the diagnosis of the response phase and the climbing phase of the oxygen sensor in the unilateral diagnosis degradation can be further distinguished, thereby improving the precision of the oxygen sensor diagnosis.

[0072] In some other embodiments, a third time threshold may be set, and the sum of the first target response time integral and the first target climbing time integral may be compared with the third time threshold.

[0073] If the sum of the first target response time integral and the first target climb time integral is greater than or equal to the third time threshold, the lean-to-rich unilateral degradation diagnosis result of the oxygen sensor may be determined as a lean-to-rich response degradation fault.

[0074] If the sum of the first target response time integral and the first target climb time integral is less than the third time threshold, the lean-to-rich unilateral degradation diagnosis result of the oxygen sensor may be determined as a lean-to-rich response normal.

[0075] In this way, it is possible to save resources occupied by oxygen sensor diagnosis on the basis of realizing accurate diagnosis of unilateral degradation from lean to rich of the oxygen sensor.

[0076] The first time threshold, the second time threshold and the third time threshold can be set based on the characteristics of the oxygen sensor. In some embodiments, the first time threshold and the second time threshold can be set to 0.7s, and the third time threshold can be set to 1.4s, but is not limited thereto.

[0077] Step 205: Determine a rich-to-lean unilateral degradation diagnosis result of the oxygen sensor according to the delay time integral and the climb time integral of each rich-to-lean unilateral degradation diagnosis in the diagnostic task.

[0078] The difference between step 205 and step 204 is that step 204 focuses on the diagnosis of the lean to rich state of the oxygen sensor, while step 205 focuses on the diagnosis of the rich to lean state of the oxygen sensor. The implementation logic of the two is the same. Therefore, the specific implementation of step 205 can refer to the implementation of step 204 and will not be repeated here.

[0079] It should be noted that, for two different types of unilateral degradation diagnosis, the first time threshold, the second time threshold and the third time threshold set therefor may be the same or different, and may be set specifically according to actual needs, and the embodiments of the present application are not limited to this.

[0080] After obtaining the rich-to-lean unilateral degradation diagnosis results and the lean-to-rich unilateral degradation diagnosis results of the oxygen sensor, if there is a lean-to-rich response degradation fault or a rich-to-lean response degradation fault, the corresponding fault code can be reported to remind the driver to maintain the oxygen sensor.

[0081] The oxygen sensor diagnosis method of the embodiment of the present application can actively diagnose the oxygen sensor when the diagnostic activation conditions of the oxygen sensor are met. Specifically, the actual combustion state of the engine can be determined according to the measured Lambda of the oxygen sensor, and then the diagnostic task of the oxygen sensor can be executed according to the actual combustion state of the engine. For each unilateral degradation diagnosis in the diagnostic task, the delay time integral and the climbing time integral in the process of the measured Lambda of the oxygen sensor changing to the target Lambda corresponding to the unilateral degradation diagnosis are obtained. Then, according to the delay time integral and the climbing time integral of each lean-to-rich unilateral degradation diagnosis in the diagnostic task, the unilateral degradation diagnosis result of the oxygen sensor from lean to rich is determined; according to the delay time integral and the climbing time integral of each lean-to-rich unilateral degradation diagnosis in the diagnostic task, the unilateral degradation diagnosis result of the oxygen sensor from lean to rich is determined. It can be seen that in the process of diagnosing the oxygen sensor, the embodiment of the present application can not only realize the unilateral degradation diagnosis of the oxygen sensor from lean to rich, and the unilateral degradation diagnosis of rich to lean, so as to improve the diagnostic reliability of the oxygen sensor. In the unilateral degradation diagnosis, the response phase and the climbing phase of the oxygen sensor are further distinguished, and the delay time integral of the oxygen sensor in the response phase and the climbing time integral of the oxygen sensor in the climbing phase are obtained, wherein the delay time integral can characterize the response delay of the oxygen sensor, and the climbing time integral can characterize the climbing rate of the oxygen sensor. In this way, the lean-to-rich unilateral degradation diagnosis result of the oxygen sensor is determined by the delay time integral and the climbing time integral of each lean-to-rich unilateral degradation diagnosis in the diagnostic task, and the rich-to-lean unilateral degradation diagnosis result of the oxygen sensor is determined by the delay time integral and the climbing time integral of each rich-to-lean unilateral degradation diagnosis in the diagnostic task, thereby further improving the diagnostic reliability of the oxygen sensor.

[0082] In some embodiments, for each of the unilateral degradation diagnosis, obtaining the delay time integral and the climb time integral in the process of the measured Lambda of the oxygen sensor changing to the target Lambda corresponding to the unilateral degradation diagnosis includes:

[0083] For each of the unilateral degradation diagnoses, obtaining a response time point at which the engine responds to the target Lambda;

[0084] Determining a target time point according to the response time point;

[0085] Obtaining a time integral of a measured Lambda of the oxygen sensor from a first Lambda to a second Lambda, and obtaining a delay time integral of the oxygen sensor in the unilateral degradation diagnosis;

[0086] Obtaining a time integral of the measured Lambda of the oxygen sensor from the second Lambda to the third Lambda, and obtaining a climbing time integral of the oxygen sensor in the unilateral degradation diagnosis;

[0087] The first Lambda is the Lambda measured by the oxygen sensor at the target time point, the second Lambda is separated from the first Lambda by a first value, the third Lambda is separated from the second Lambda by a second value, and the first value and the second value are determined based on the characteristics of the oxygen sensor.

[0088] When performing unilateral degradation diagnosis, the target Lambda corresponding to the unilateral degradation diagnosis can be determined first, and then the target Lambda can be transmitted to the engine so that the engine adjusts the fuel supply so that the Lambda changes to the target Lambda. After receiving the target Lambda, the engine will respond to the target Lambda, that is, adjust the fuel supply, and the time point when the engine responds to the target Lambda can be recorded and used as the response time point or response starting point.

[0089] In this embodiment, the target time point may be determined according to the above response time point, and the target time point is the starting point of the delay phase (hereinafter referred to as the delay starting point).

[0090] In some implementations, determining the target time point according to the response time point may include:

[0091] acquiring a flow delay of the gas discharged from the exhaust pipe to flow to the oxygen sensor;

[0092] A time point that is after the response time point and is separated from the response time point by the airflow delay is determined as the target time point.

[0093] The embodiment of the present application does not limit the method of obtaining the above-mentioned airflow delay, and the above-mentioned airflow delay can be obtained by calculating relevant parameters, or can be calibrated by empirical data or experimental data. In one example, the above-mentioned airflow delay can be the time taken for the engine to rotate 720 degrees, but is not limited thereto.

[0094] In this embodiment, when determining the delay starting point based on the response time point, the airflow delay of the gas discharged from the exhaust pipe to the oxygen sensor is further considered, and the time point that is located after the response time point and separated from the response time point by the airflow delay is used as the delay starting point. In this way, the influence of the airflow delay on the response delay of the oxygen sensor can be eliminated, the diagnostic reliability of the response delay of the oxygen sensor can be improved, and the reliability of the unilateral degradation diagnosis of the oxygen sensor can be improved.

[0095] Of course, in other embodiments, the response time point can also be directly used as the delay starting point, but in order to eliminate the influence of airflow delay on the response delay of the oxygen sensor, the airflow delay needs to be considered when setting the above-mentioned first time threshold and the second time threshold, and the airflow delay is reflected in the above-mentioned first time threshold and the second time threshold. For example: assuming that there is no airflow delay, the first time threshold is set to t1, then the first time threshold in this embodiment should be set to t1+Δt, where Δt represents the airflow delay.

[0096] In this embodiment, the delay stage and the slope stage can be set to be continuous, the slope stage is located after the delay stage, and the end point of the delay stage (hereinafter referred to as the delay end point) is the starting point of the slope stage (hereinafter referred to as the slope starting point).

[0097] To determine the delay end point and the slope end point, a first value (ΔLambda1) and a second value (ΔLambda2) may be preset, ΔLambda1 representing the difference in Lambda during the delay phase, and ΔLambda2 representing the difference in Lambda during the slope phase. ΔLambda1 and ΔLambda2 may be set based on the characteristics of the oxygen sensor. In one example, ΔLambda1 may be set to 0.1, and ΔLambda2 may be set to 0.2, but are not limited thereto.

[0098] In this way, after obtaining the measured Lambda (i.e., the first Lambda) of the oxygen sensor at the start of the delay, the second Lambda can be calculated using the first Lambda and ΔLambda1, and the third Lambda can be calculated using the second Lambda and ΔLambda2. For the unilateral degradation diagnosis from lean to rich, the second Lambda = first Lambda - ΔLambda1, and the third Lambda = second Lambda - ΔLambda2. For the unilateral degradation diagnosis from rich to lean, the second Lambda = first Lambda + ΔLambda1, and the third Lambda = second Lambda + ΔLambda2.

[0099] It can be understood that the second Lambda is the Lambda corresponding to the end point of delay and the start point of slope, and the third Lambda is the Lambda corresponding to the end point of slope. Therefore, the time integral of the lambda measured by the oxygen sensor from the first lambda to the second lambda is the delay time integral of the oxygen sensor; the time integral of the lambda measured by the oxygen sensor from the second lambda to the third lambda is the climbing time integral of the oxygen sensor.

[0100] In some embodiments, Figure 4 As shown, time integration can be performed from the response start point, and a time integration line based on the response start point can be drawn in the Lambda-time test data of the unilateral degradation diagnosis. In this embodiment, the delay start point, delay end point, and slope end point can be found on the time integration line based on the response start point in the above manner, and then the difference between the delay start point and the delay end point is used as the delay time integral, and the difference between the slope start point and the slope end point is used as the climbing time integral.

[0101] In other embodiments, the time integration may be started from the first Lambda, and then the time integration may be ended when it is detected that the measured Lambda of the oxygen sensor reaches the second Lambda, and this time integration is used as the delay time integration. At the same time, a new time integration may be started from the second Lambda, and then the time integration may be ended when it is detected that the measured Lambda of the oxygen sensor reaches the third Lambda, and this time integration is used as the climbing time integration.

[0102] In the above embodiment, during the unilateral degradation diagnosis process, the delay starting point is determined according to the response time point when the engine responds to the table Lambda of the unilateral degradation diagnosis, and then the delay end point and the slope end point are determined using ΔLambda1 and ΔLambda2. In this way, the acquired delay time integral can accurately reflect the response delay of the oxygen sensor, and the climb time integral can accurately reflect the climb rate of the oxygen sensor, thereby improving the diagnostic reliability of the oxygen sensor.

[0103] It should be noted that the above-mentioned methods for determining the delay starting point, delay end point, slope starting point and slope end point are only examples and are not intended to limit them. In some embodiments, the delay starting point can be determined based on the time point of outputting the target Lambda, such as determining a time point that is after the output time point of the target Lambda and is separated from it by a first time length as the delay starting point. The first time length may include the engine's response time to the target Lambda and the above-mentioned airflow delay. In some embodiments, the delay stage and the slope stage may be discontinuous, and the delay end point and the slope starting point may be separated by a second time length. The second time length may be pre-set based on demand, and the embodiments of the present application do not limit this.

[0104] In some embodiments, performing the oxygen sensor diagnostic task may include:

[0105] Determine the target Lambda corresponding to the i-th unilateral degradation diagnosis; where i is a positive integer less than or equal to 2N;

[0106] Executing the i-th unilateral degradation diagnosis according to the target Lambda corresponding to the i-th unilateral degradation diagnosis;

[0107] When a deviation between a measured Lambda of the oxygen sensor and a target Lambda corresponding to the i-th unilateral degradation diagnosis is less than a first deviation threshold, determining that the i-th unilateral degradation diagnosis is completed;

[0108] If i is less than 2N and the diagnosis activation condition is still satisfied, i is updated to i+1, and the process returns to executing the process of determining the target Lambda corresponding to the i-th unilateral degradation diagnosis until i is equal to 2N.

[0109] In this embodiment, for each unilateral degradation diagnosis, the corresponding target Lambda can be determined first, and then the unilateral degradation diagnosis is performed according to the target Lambda: the target Lambda is transmitted to the engine, and then the delay time integral and the climb time integral are obtained by monitoring the measured Lambda of the oxygen sensor.

[0110] In addition, by calculating the deviation between the measured Lambda of the oxygen sensor and the target Lambda and comparing it with the first deviation threshold, it is determined whether the current unilateral degradation diagnosis is completed.

[0111] If the deviation is greater than or equal to the first deviation threshold, the current unilateral degradation diagnosis may be considered incomplete, and the measured Lambda of the oxygen sensor may continue to be monitored until the deviation between the measured Lambda of the oxygen sensor and the target Lambda is less than the first deviation threshold.

[0112] If the deviation is less than the first deviation threshold, the current unilateral degradation diagnosis can be considered completed. Afterwards, it can be determined whether the unilateral degradation diagnosis is the last unilateral degradation diagnosis of the diagnostic task. If the unilateral degradation diagnosis is the last unilateral degradation diagnosis of the diagnostic task, it can be determined that the diagnostic task is completed, and step 204 and step 205 are executed. If the unilateral degradation diagnosis is not the last unilateral degradation diagnosis of the diagnostic task, it can be monitored again whether the diagnostic activation condition of the oxygen sensor is still satisfied.

[0113] If the diagnosis activation condition is not met, the current diagnosis task is interrupted. If the diagnosis activation condition is still met, the next unilateral degradation diagnosis can be continued, and so on, until all unilateral degradation diagnoses in the diagnosis task are completed.

[0114] In this embodiment, during the execution of the diagnostic task, each time a unilateral degradation diagnosis is completed, a determination is made as to whether the diagnostic activation condition of the oxygen sensor is satisfied, and then a determination is made as to whether to interrupt the diagnostic task, thereby improving the diagnostic reliability.

[0115] Of course, in some embodiments, after the diagnostic task is started, it is not necessary to determine whether the diagnostic activation condition of the oxygen sensor is met. The specific setting can be based on actual needs, and the embodiments of the present application are not limited to this.

[0116] In the embodiment of the present application, the introduction of the diagnostic activation condition of the oxygen sensor is intended to reflect that the embodiment of the present application can realize active diagnosis of the oxygen sensor. Therefore, the embodiment of the present application does not limit the specific form of the diagnostic activation condition of the oxygen sensor. In some embodiments, the method further includes:

[0117] When it is determined that the vehicle enters an emission control closed loop, obtaining status information of the vehicle;

[0118] In a case where it is determined based on the state information that the vehicle is in a stable running state, it is determined that the diagnosis activation condition is satisfied.

[0119] In this embodiment, when the vehicle enters the emission control closed loop, it is possible to determine whether the vehicle is in a stable running state by acquiring the vehicle status information, and further determine whether the diagnostic activation condition of the oxygen sensor is met.

[0120] If the vehicle is determined to be in a stable running state based on the vehicle status information, it can be determined that the diagnosis activation condition is met and the oxygen sensor diagnosis is activated. If the vehicle is determined not to be in a stable running state based on the vehicle status information, it can be determined that the diagnosis activation condition is not met.

[0121] It is worth noting that in the embodiment of the present application, the accelerator operation will disrupt the stable operation of the vehicle. Therefore, if the accelerator operation is detected, it will trigger the vehicle to exit the diagnosis of the oxygen sensor.

[0122] The embodiments of the present application do not limit the specific form of the vehicle status information. In some embodiments, the vehicle status information may include, but is not limited to, at least one of the following: ① engine speed, ② engine intake flow, ③ engine water temperature, ④ engine torque, ⑤ ambient temperature and pressure, ⑥ front oxygen sensor status, ⑦ whether the engine is running in a steady state, and ⑧ carbon canister status. In one example, when the engine speed, intake flow, water temperature, and torque are all in a stable range, the engine is in a steady state, the carbon canister has no faults, and the oxygen sensor heating has been completed, it can be determined that the vehicle is in a stable running state, and then it is determined that the diagnostic activation conditions are met. Otherwise, it can be determined that the diagnostic activation conditions are not met.

[0123] In this embodiment, the vehicle status information is obtained to determine whether the vehicle is in a stable operating state, and then to determine whether the diagnostic activation conditions of the oxygen sensor are met. In this way, the diagnosis of the oxygen sensor can occur in the stable operating state of the vehicle, thereby improving the diagnostic reliability of the oxygen sensor.

[0124] It should be noted that the various optional implementation modes introduced in the embodiments of the present application can be implemented in combination with each other or separately if they do not conflict with each other, and the embodiments of the present application are not limited to this.

[0125] The embodiment of the present application can use the measured Lambda and reference Lambda of the oxygen sensor to determine the actual richness or leanness of the current combustion, and through precise jumps between the rich end and the lean end, realize the diagnosis of the response rate of the oxygen sensor from lean to rich and from rich to lean ends, distinguish the response rate of the delay and slope stages, and also add the recognition of jump hysteresis. During the diagnosis process, the signal oscillation in the process of Lambda rising and falling is identified and a reasonable integration window is selected, while maintaining good robustness to emissions.

[0126] For ease of understanding, a specific embodiment is used as an example for illustration:

[0127] like Figure 5 As shown, the oxygen sensor diagnosis method of the embodiment of the present application may include the following steps:

[0128] Step 501, obtaining vehicle status information.

[0129] Step 502: Determine whether the diagnosis activation condition of the front oxygen sensor is met according to the status information.

[0130] If satisfied, execute step 503; otherwise, execute step 514.

[0131] Step 503 , calculating the deviation between the measured Lambda of the front oxygen sensor and the reference Lambda.

[0132] Step 504: determine whether the engine is at the lean end based on the deviation.

[0133] If yes, execute step 505 , if no, execute step 509 .

[0134] Step 505 , determining the target Lambda corresponding to the lean end, and performing lean-to-rich unilateral degradation diagnosis.

[0135] The target Lambda corresponding to the lean end, that is, the target Lambda corresponding to the unilateral degradation diagnosis from lean to rich.

[0136] Step 506 , obtaining the time integral of the delay phase and the slope phase from lean to rich.

[0137] Step 507 : when the lean-to-rich unilateral degradation diagnosis is completed, if the time integrals of the delay phase and the slope phase of the lean-to-rich phase do not meet the requirements, a lean-to-rich response degradation fault is reported.

[0138] Step 508 , determining whether the diagnostic activation condition of the front oxygen sensor is satisfied.

[0139] If so, execute step 509; otherwise, execute step 514.

[0140] Step 509 , determine the target Lambda corresponding to the rich end, and perform rich-to-lean unilateral degradation diagnosis.

[0141] The target Lambda corresponding to the rich end, that is, the target Lambda corresponding to the unilateral degradation diagnosis from rich to lean.

[0142] Step 510, obtaining the time integral of the delay phase and the slope phase from rich to lean.

[0143] Step 511 , when the rich-to-lean unilateral degradation diagnosis is completed, if the time integrals of the rich-to-lean delay phase and the slope phase do not meet the requirements, a rich-to-lean response degradation fault is reported.

[0144] Step 512, determining whether the diagnostic activation condition of the front oxygen sensor is satisfied.

[0145] If so, execute step 505; otherwise, execute step 514.

[0146] Step 513, exit diagnostic control.

[0147] Step 514, enter normal Lambda control.

[0148] In one example, the normal Lambda control may be between 0.99-1.01, and the Lambda control during the diagnosis process may be between 0.97-1.03, that is, the target Lambda corresponding to the rich end may be set to 1.03, and the target Lambda corresponding to the lean end may be set to 0.97. In addition, in step 503, if the measured Lambda of the front oxygen sensor is greater than or equal to 1, the reference Lambda may be 1.03; if the measured Lambda of the front oxygen sensor is less than 1, the reference Lambda may be 0.97.

[0149] exist Figure 5 In the process, if the active diagnosis condition of the current rich or lean end is completed and the diagnosis activation conditions are still met, you can flip to the next lean or rich end. If the diagnosis activation conditions are not met, you can exit the diagnosis control and switch to normal Lambda control.

[0150] During the Lambda flipping process, if the response time integral calculated within the response time window from the rich to the lean end (distinguishing the response rate of the delay and slope stages) is higher than the threshold value (0.7-can be calibrated according to the sensor characteristics), a rich to lean response degradation fault may be reported; if the response time integral calculated within the response time window from the lean to the rich end (distinguishing the response rate of the delay and slope stages) is higher than the threshold value (0.7-can be calibrated according to the sensor characteristics), a lean to rich response degradation fault may be reported.

[0151] It can be seen that the embodiment of the present application can realize the active increase of the Lambda's rich-lean reversal amplitude on the basis of meeting the emission requirements through unilateral degradation active diagnosis, so as to realize the diagnosis of the air-fuel ratio response rate of the front oxygen sensor in the exhaust gas monitoring, and can accurately calculate the actual response delay and climb rate of the front oxygen sensor by dividing the Lambda response time and climb time of the front oxygen sensor to evaluate whether the performance of the front oxygen sensor has reached the degradation threshold, thereby reporting a fault code to remind the driver that the front oxygen sensor needs to be maintained, thereby improving the working reliability of the oxygen sensor.

[0152] Based on the oxygen sensor diagnostic method provided in the above embodiment, the present application also provides a specific implementation of an oxygen sensor diagnostic device. Please refer to the following embodiment.

[0153] See also Figure 6 , the oxygen sensor diagnostic device provided in the embodiment of the present application may include:

[0154] A first determination module 601 is used to determine the actual combustion state of the engine according to the measured Lambda of the oxygen sensor when the diagnosis activation condition of the oxygen sensor is met;

[0155] an execution module 602, configured to execute a diagnosis task of the oxygen sensor when the actual combustion state is a lean state or a rich state; wherein the diagnosis task includes N diagnosis cycles, each of which includes a lean-to-rich unilateral degradation diagnosis and a rich-to-lean unilateral degradation diagnosis, and N is a positive integer;

[0156] The acquisition module 603 is used to acquire, for each of the unilateral degradation diagnosis, a delay time integral and a climb time integral in a process in which the measured Lambda of the oxygen sensor changes to the target Lambda corresponding to the unilateral degradation diagnosis; wherein the delay time integral is used to characterize the response delay of the oxygen sensor, and the climb time integral is used to characterize the climb rate of the oxygen sensor;

[0157] A second determination module 604 is configured to determine a lean-to-rich unilateral degradation diagnosis result of the oxygen sensor according to the delay time integral and the climb time integral of each of the lean-to-rich unilateral degradation diagnosis in the diagnosis task;

[0158] The third determination module 605 is configured to determine a rich-to-lean unilateral degradation diagnosis result of the oxygen sensor according to the delay time integral and the climb time integral of each rich-to-lean unilateral degradation diagnosis in the diagnosis task.

[0159] The oxygen sensor diagnostic device provided in the embodiment of the present application can implement each process implemented by the oxygen sensor diagnostic device in the method embodiment, and will not be described again here to avoid repetition.

[0160] Figure 7 A schematic diagram of the hardware structure of the oxygen sensor diagnosis provided in an embodiment of the present application is shown.

[0161] The oxygen sensor diagnostic device may include a processor 701 and a memory 702 storing computer program instructions.

[0162] Specifically, the processor 701 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0163] The memory 702 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 702 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 702 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 702 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 702 is a non-volatile solid-state memory.

[0164] The memory may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Therefore, generally, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of the present disclosure.

[0165] The processor 701 implements any one of the oxygen sensor diagnosis methods in the above embodiments by reading and executing computer program instructions stored in the memory 702 .

[0166] In one example, the oxygen sensor diagnostic device may further include a communication interface 707 and a bus 710. Figure 7 As shown, the processor 701, the memory 702, and the communication interface 707 are connected via a bus 710 and communicate with each other.

[0167] The communication interface 707 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0168] Bus 710 includes hardware, software or both, and couples the components of oxygen sensor diagnostic equipment to each other. For example, but not limitation, the bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front-side bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnect (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. Where appropriate, bus 710 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnection.

[0169] In addition, in combination with the oxygen sensor diagnostic method in the above embodiment, the embodiment of the present application can provide a computer storage medium to implement. The computer storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any one of the oxygen sensor diagnostic methods in the above embodiment is implemented.

[0170] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0171] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0172] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0173] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0174] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. An oxygen sensor diagnostic method, characterized in that: include: When the diagnostic activation condition of the oxygen sensor is met, determining the actual combustion state of the engine according to the measured Lambda of the oxygen sensor; According to the actual combustion state, the diagnostic task of the oxygen sensor is executed; wherein the diagnostic task includes N diagnostic cycles, each of which includes a lean-to-rich unilateral degradation diagnosis and a rich-to-lean unilateral degradation diagnosis, and N is a positive integer; when the actual combustion state is a lean state, the first unilateral degradation diagnosis of the diagnostic task is the lean-to-rich unilateral degradation diagnosis, and when the actual combustion state is a rich state, the first unilateral degradation diagnosis of the diagnostic task is the rich-to-lean unilateral degradation diagnosis; For each of the unilateral degradation diagnosis, a delay time integral and a climbing time integral are obtained in a process in which the measured Lambda of the oxygen sensor changes to the target Lambda corresponding to the unilateral degradation diagnosis; wherein the delay time integral is used to characterize the response delay of the oxygen sensor, and the climbing time integral is used to characterize the climbing rate of the oxygen sensor; determining a lean-to-rich unilateral degradation diagnosis result of the oxygen sensor according to the delay time integral and the ramp-up time integral of each of the lean-to-rich unilateral degradation diagnosis in the diagnostic task; A rich-to-lean unilateral degradation diagnosis result of the oxygen sensor is determined based on the delay time integral and the climb time integral of each of the rich-to-lean unilateral degradation diagnosis in the diagnostic task.

2. The method according to claim 1, characterized in that For each of the unilateral degradation diagnosis, obtaining the delay time integral and the climbing time integral in the process of the measured Lambda of the oxygen sensor changing to the target Lambda corresponding to the unilateral degradation diagnosis includes: For each of the unilateral degradation diagnoses, obtaining a response time point at which the engine responds to the target Lambda; Determining a target time point according to the response time point; Obtaining a time integral of a measured Lambda of the oxygen sensor from a first Lambda to a second Lambda, and obtaining a delay time integral of the oxygen sensor in the unilateral degradation diagnosis; Obtaining a time integral of the measured Lambda of the oxygen sensor from the second Lambda to the third Lambda, and obtaining a climbing time integral of the oxygen sensor in the unilateral degradation diagnosis; The first Lambda is the Lambda measured by the oxygen sensor at the target time point, the second Lambda is separated from the first Lambda by a first value, the third Lambda is separated from the second Lambda by a second value, and the first value and the second value are determined based on the characteristics of the oxygen sensor.

3. The method according to claim 2, characterized in that The determining the target time point according to the response time point includes: acquiring a flow delay of the gas discharged from the exhaust pipe to flow to the oxygen sensor; A time point that is after the response time point and is separated from the response time point by the airflow delay is determined as the target time point.

4. The method according to claim 1, characterized in that The performing of the diagnostic task of the oxygen sensor includes: Determine the target Lambda corresponding to the i-th unilateral degradation diagnosis; where i is a positive integer less than or equal to 2N; Executing the i-th unilateral degradation diagnosis according to the target Lambda corresponding to the i-th unilateral degradation diagnosis; When a deviation between a measured Lambda of the oxygen sensor and a target Lambda corresponding to the i-th unilateral degradation diagnosis is less than a first deviation threshold, determining that the i-th unilateral degradation diagnosis is completed; If i is less than 2N and the diagnosis activation condition is still satisfied, i is updated to i+1, and the process returns to executing the process of determining the target Lambda corresponding to the i-th unilateral degradation diagnosis until i is equal to 2N.

5. The method according to claim 1, characterized in that Determining the actual combustion state of the engine according to the measured Lambda of the oxygen sensor includes: Determining an initial combustion state of the engine according to a first comparison result of the measured Lambda of the oxygen sensor and the preset Lambda; wherein, when the measured Lambda of the oxygen sensor is greater than or equal to the preset Lambda, the initial combustion state is a lean state; and when the measured Lambda of the oxygen sensor is less than the preset Lambda, the initial combustion state is a rich state; When the measured Lambda of the oxygen sensor meets the target condition, or the time when the measured Lambda of the oxygen sensor does not meet the target condition is greater than a preset time, the initial combustion state is determined as the actual combustion state of the engine; wherein the target condition is that the deviation between the measured Lambda of the oxygen sensor and the reference Lambda corresponding to the initial combustion state is less than a second deviation threshold; and the reference Lambda corresponding to the lean state is greater than the reference Lambda corresponding to the rich state.

6. The method according to claim 1, characterized in that The method further comprises: When it is determined that the vehicle enters an emission control closed loop, obtaining status information of the vehicle; In a case where it is determined based on the state information that the vehicle is in a stable running state, it is determined that the diagnosis activation condition is satisfied.

7. An oxygen sensor diagnostic device, characterized in that: The device comprises: a first determination module, configured to determine an actual combustion state of the engine according to Lambda measured by the oxygen sensor when a diagnosis activation condition of the oxygen sensor is met; an execution module, configured to execute a diagnostic task of the oxygen sensor when the actual combustion state is a lean state or a rich state; wherein the diagnostic task includes N diagnostic cycles, each of which includes a lean-to-rich unilateral degradation diagnosis and a rich-to-lean unilateral degradation diagnosis, and N is a positive integer; an acquisition module, configured to acquire, for each of the unilateral degradation diagnosis, a delay time integral and a climb time integral in a process in which the measured Lambda of the oxygen sensor changes to the target Lambda corresponding to the unilateral degradation diagnosis; wherein the delay time integral is used to characterize the response delay of the oxygen sensor, and the climb time integral is used to characterize the climb rate of the oxygen sensor; a second determination module, configured to determine a lean-to-rich unilateral degradation diagnosis result of the oxygen sensor according to the delay time integral and the climb time integral of each of the lean-to-rich unilateral degradation diagnosis in the diagnosis task; The third determination module is configured to determine a rich-to-lean unilateral degradation diagnosis result of the oxygen sensor according to the delay time integral and the climb time integral of each of the rich-to-lean unilateral degradation diagnosis in the diagnosis task.

8. An oxygen sensor diagnostic device, characterized in that: The device comprises: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the oxygen sensor diagnosis method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the oxygen sensor diagnosis method according to any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the oxygen sensor diagnosis method according to any one of claims 1 to 6.

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