Driving instruction system and driving instruction method

Abstract

To appropriately execute a performance recovery operation.SOLUTION: An operation instruction system includes: a plurality of control systems each having an engine system including an engine and an aftertreatment device for purifying exhaust gas of the engine and a control device automatically controlling a performance recovery operation of the engine system, which is an operation for recovering performance of the aftertreatment device; and a monitoring device for remotely monitoring the plurality of control systems. The monitoring device includes: an acquisition section periodically acquiring operation data indicating an operating state of each of the engine systems from each of the control systems; a prediction section predicting deterioration of the performance of the aftertreatment device on the basis of the operation data; and an operation instruction section instructing the control system having the aftertreatment device in which predetermined performance deterioration is predicted to increase frequency of the performance recovery operation.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present disclosure relates to a driving instruction system and a driving instruction method.

Background Art

[0002] Patent Document 1 describes a catalyst abnormality detection device as follows. That is, the catalyst abnormality detection device described in Patent Document 1 first adjusts the upstream exhaust temperature of the oxidation catalyst to a specific temperature desirable for determination, and then supplies unburned fuel to the oxidation catalyst. Then, this catalyst abnormality detection device determines the deterioration of the oxidation catalyst by utilizing the difference in the amount of change in the downstream exhaust temperature with respect to the upstream exhaust temperature between normal products and deteriorated products.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] By the way, in a post-treatment device for purifying engine exhaust gas, an operation (hereinafter referred to as a performance recovery operation) for recovering the deteriorated performance of the post-treatment device may be automatically executed by raising the temperature of the exhaust gas. When the performance recovery operation is appropriately executed, the performance of the post-treatment device can be maintained. On the other hand, when the performance recovery operation is not appropriately executed, the performance deterioration progresses, and the performance recovery operation may continue for a long time, or the operation may have to be stopped and the performance recovery operation may have to be executed in a stable state.

[0005] The present invention has been made in view of the above circumstances, and an object thereof is to provide a driving instruction system and a driving instruction method capable of appropriately executing a performance recovery operation.

Means for Solving the Problems

[0006] The operation instruction system of the present disclosure comprises a plurality of control systems, each having an engine system including an engine and an aftertreatment device for purifying exhaust gas from the engine, and a control device that automatically controls a performance recovery operation of the engine system, which is an operation to restore the performance of the aftertreatment device, and a monitoring device that remotely monitors the plurality of control systems, wherein the monitoring device comprises an acquisition unit that periodically acquires operation data indicating the operating status of each engine system from each of the control systems, a prediction unit that predicts a decrease in the performance of the aftertreatment device based on the operation data, and an operation instruction unit that instructs the control system having the aftertreatment device for which a predetermined decrease in performance is predicted to increase the frequency of the performance recovery operation. [Effects of the Invention]

[0007] The operation instruction system and operation instruction method of this disclosure can appropriately perform performance recovery operation. [Brief explanation of the drawing]

[0008] [Figure 1] This is a system diagram showing an example configuration of a driving instruction system according to the present disclosure. [Figure 2] This is a block diagram showing an example of the configuration of a vehicle according to the embodiment of this disclosure. [Figure 3] This is a block diagram showing an example of the functional configuration of a driving instruction system according to an embodiment of the present disclosure. [Figure 4] This is a block diagram illustrating a predictive model according to an embodiment of this disclosure. [Figure 5] This is a schematic diagram illustrating an example of the configuration of operating data according to the embodiment of this disclosure. [Figure 6] This is a schematic diagram illustrating another example of the configuration of the operating data according to the embodiments of this disclosure. [Figure 7] This is a schematic diagram illustrating another example of the configuration of the operating data according to the embodiments of this disclosure. [Figure 8]This flowchart shows an example of the operation of a driving instruction system according to the embodiment of this disclosure. [Modes for carrying out the invention]

[0009] Embodiments of this disclosure will be described below with reference to the drawings. In each drawing, the same or corresponding components are given the same reference numerals, and their descriptions will be omitted as appropriate.

[0010] (Basic configuration of the driver instruction system) The basic configuration of the driving instruction system according to the embodiment of this disclosure will be described with reference to Figures 1 and 2. Figure 1 is a system diagram showing an example configuration of the driving instruction system 500 according to the embodiment of this disclosure. Figure 2 is a block diagram showing an example configuration of the vehicle 20 according to the embodiment of this disclosure.

[0011] The driving instruction system 500 shown in Figure 1 comprises a server 300 and a plurality of vehicles 20A to 20D. Vehicles 20A to 20D are, for example, construction vehicles such as hydraulic excavators 20A and 20C, dump trucks 20B, and bulldozers 20D. However, vehicles 20A to 20D are not limited to construction vehicles. When referring to vehicles 20A to 20D collectively, they are referred to as vehicle 20.

[0012] As shown in Figure 2, the vehicle 20 comprises an engine system 10, an engine controller 100, a body controller 200, and a monitor 8. The engine system 10 comprises an engine 1, an exhaust pipe 3, a DPF device 5, and an HC dozer 7.

[0013] The DPF device 5 is an example of an aftertreatment device for purifying the exhaust gas of the engine 1 according to this disclosure. The vehicle 20 is an example of a control system according to this disclosure. Furthermore, a configuration combining the engine controller 100 and the vehicle body controller 200 is an example of a control device according to this disclosure.

[0014] In Figure 2, etc., the configuration of the DPF device 5 in the engine system 10 of this embodiment (or the controller 100 and the vehicle body controller 200) is mainly shown, and the configuration of other functions such as fuel injection control is omitted from the illustration as appropriate. Also, HC is a general term for organic compounds containing carbon and hydrogen.

[0015] Engine 1 is an example of an internal combustion engine configuration, and in this embodiment, it is, for example, a multi-cylinder diesel engine. Engine 1 includes components such as a fuel injection system and a turbocharger. The turbocharger is a supercharger that uses the exhaust (exhaust gas) of Engine 1 to compress the intake air of Engine 1. The exhaust pipe 3 discharges the exhaust from Engine 1 to the atmosphere through the DPF device 5.

[0016] The DPF device 5 is a device that purifies particulate matter contained in the exhaust of the engine 1. The DPF device 5 comprises a DOC 51 and a DPF 52 installed in the exhaust pipe 3 of the engine 1. DOC 51 is a DOC (Diesel Oxidation Catalyst). DPF 52 is a DPF (Diesel Particulate Filter), which is a filter that collects PM (Particulate Matter) in the exhaust of the engine 1. The DPF device 5 regenerates the DPF 52 through the action of DOC 51. The DPF device 5 removes soot by oxidizing the soot collected downstream with nitrogen dioxide converted by DOC 51, which is installed upstream of the DPF 52, into carbon dioxide.

[0017] Furthermore, the DPF device 5 includes a DOC inlet temperature sensor 94 for detecting the exhaust temperature at the inlet of the DOC 51, a DOC outlet temperature sensor 95 for detecting the exhaust temperature at the outlet of the DOC 51, and a pair of pressure sensors 96 and 97 for detecting the differential pressure between the inlet and outlet of the DPF 52. Hereinafter, this differential pressure between the inlet and outlet of the DPF 52 will also be referred to as the DPF differential pressure or DPF pressure loss. The detected values ​​from the DOC inlet temperature sensor 94, the DOC outlet temperature sensor 95, and the pressure sensors 96 and 97 are output to the engine controller 100.

[0018] The HC dozer 7 is an exhaust pipe fuel injection device that injects fuel (HC) into the exhaust pipe 3 upstream of the DOC51 (hereinafter referred to as HC doping, etc.). The HC doping by the HC dozer 7 is controlled by the engine controller 100.

[0019] The monitor 8 has, for example, a display panel and an input panel, functions as a display device and an input device, displays predetermined characters and images according to instructions from the engine controller 100 or the vehicle body controller 200, and outputs a signal corresponding to the input operation of the user (operator) to the engine controller 100 or the vehicle body controller 200.

[0020] The engine controller 100 and the vehicle body controller 200 can be configured using a computer such as a microcomputer, and peripheral circuits and peripheral devices of the computer. The engine controller 100 controls the engine 1 and the HC dozer 7 according to instructions from the vehicle body controller 200, for example.

[0021] The vehicle body controller 200 controls each part of the vehicle 20 other than the engine 1 and the DPF device 5, for example. The vehicle body controller 200 also shares operation data indicating the operation state of the engine system 10 with the engine controller 100. The vehicle body controller 还定期向服务器300发送运行数据。

[0022] The operation data is data indicating the operation state of the engine system 10. The operation data includes data corresponding to at least one of, for example, the fuel consumption of the engine, the flow rate of the exhaust gas, and the temperature of the exhaust gas. The fuel consumption of the engine corresponds to the load factor of the engine. The temperature of the exhaust gas is, for example, the exhaust temperature at the inlet or outlet of the DOC51. The fuel consumption of the engine and the flow rate of the exhaust gas can be calculated according to the operation state of the engine 1, for example.

[0023] Furthermore, the operating data includes data indicating the occurrence of predetermined events corresponding to a performance degradation of the DPF device 5. These predetermined events correspond to, for example, a performance degradation of the DPF device 5 that requires additional action beyond the normal automatic performance recovery operation. The performance recovery operation includes the regeneration operation and drying operation described later. Additional action includes, for example, increasing the duration of the performance recovery operation or manually performing the regeneration operation. Normal performance recovery operation refers to a performance recovery operation performed for a predetermined duration.

[0024] Furthermore, the occurrence of a predetermined event includes the generation of a fault code corresponding to at least one of the following: DOC regeneration failure, DPF regeneration failure, or DOC blockage abnormality. The fault code corresponding to DOC regeneration failure is data generated by the engine controller 100 when the blockage of DOC 51 is not resolved even after performing the drying operation described later. The fault code corresponding to DPF regeneration failure is data generated by the engine controller 100 when the performance degradation of DPF 52 is not resolved even after performing the regeneration operation described later. This fault code corresponding to DPF regeneration failure corresponds to the fault code issued when an accumulation of PM exceeding a predetermined amount is detected in DPF 52 after the regeneration operation. If a regeneration failure occurs in DPF 52, one possible cause is a performance degradation of DOC 51. Therefore, this fault code corresponding to DPF regeneration failure also corresponds to the fault code issued when a performance degradation of DOC 51 is detected. The fault code corresponding to DOC blockage abnormality is data generated by the engine controller 100 when a blockage of DOC 51 is detected, as described later. The fault code corresponding to this DOC blockage abnormality corresponds to the fault code issued when a performance degradation of DOC51 is detected.

[0025] Furthermore, the vehicle controller 200 receives an operating instruction from the server 300 to increase the frequency of performance recovery operation. When the vehicle controller 200 receives an operating instruction to increase the frequency of performance recovery operation, it increases the frequency of normal performance recovery operation performed by the engine controller 100.

[0026] In the engine system 10 of this embodiment, regeneration operation (DPF regeneration) is performed periodically to burn off PM accumulated in the DPF 52. Regeneration operation is performed to restore the purification performance of the deteriorated DPF device 5. During regeneration operation, HC (fuel) is injected into the exhaust gas, causing the temperature of the DPF device 5 to rise. In other words, during regeneration operation, the temperature of the exhaust gas and the temperature of the DOC 51 are forcibly increased. Regeneration operation is performed, for example, by post-injection to mix a small amount of fuel into the exhaust gas within the engine 1, or by a combination of post-injection and HC dosing by an HC dozer 7 into the exhaust pipe 3 upstream of the DPF device 5, or by HC dosing, thereby burning HC inside the DOC 51 located upstream of the DPF 52 and raising the temperature of the DPF 52.

[0027] In this embodiment, the engine system 10 includes automatic regeneration and stationary manual regeneration (manual regeneration). Automatic regeneration is a regeneration operation that is automatically performed by the engine controller 100, etc., in the normal operating state when certain conditions are met. Here, the normal operating state is a state in which normal operation and work can be performed without forcibly fixing the engine speed, etc. Stationary manual regeneration is a regeneration operation that is performed by the engine controller 100, etc., at any timing when regeneration is necessary in response to user operation. Stationary manual regeneration is a regeneration operation that, with the user's permission, stops normal operation and restores the performance of the DPF device 5 when the exhaust temperature does not rise sufficiently in the normal operating state and the temperature of the DPF device 5 cannot be stably controlled to the target temperature. In stationary manual regeneration, the engine controller 100, etc., first uses the monitor 8 to notify the user that it is in a state where stationary manual regeneration can be performed and to request that it be performed. In response, when the user issues a command using the monitor 8 to perform stationary manual regeneration, the engine controller 100 fixes the engine speed to a certain rotation, raises the exhaust temperature, and performs the regeneration operation.

[0028] Automatic regeneration is performed, for example, periodically at predetermined intervals. Automatic regeneration is also performed, for example, when an accumulation of PM exceeding a predetermined amount is detected (estimated) in the DPF 52 based on the differential pressure of the DPF 52 detected by pressure sensors 96 and 97. Automatic regeneration is performed, for example, by feedback control of the DOC outlet temperature through the control of post-injection of the engine 1 and the control of the HC dosing amount by the HC dozer 7, so that the DOC outlet temperature matches, for example, a predetermined regeneration target temperature. However, HC is dosed (injected) only after the DOC inlet temperature reaches the temperature at which the catalyst contained in the DOC 51 is activated (light-off temperature, for example, about 250°C).

[0029] Furthermore, in the engine system 10 of this embodiment, in addition to the regeneration operation, a drying operation (DOC drying operation) is automatically and periodically (or at predetermined intervals) performed for a predetermined duration by the engine controller 100 or the like to restore the performance of DOC51. The drying operation is an operation that dries out DOC51 by raising the temperature of the exhaust gas without injecting fuel into the exhaust gas. Depending on the usage conditions of the engine 1, PM may accumulate on the front surface of the honeycomb structure of DOC51. PM accumulates, for example, mixed with unburned fuel. If the front surface of DOC51 becomes blocked due to PM accumulation, the oxidation performance of DOC51 decreases. When the oxidation performance of DOC51 decreases, problems may arise such as not being able to raise the temperature sufficiently during DPF regeneration, or unburned fuel passing through DOC51. To restore this, a DOC drying operation is performed to dry out DOC51 by raising the exhaust temperature of the engine 1. During dry operation, the exhaust temperature of engine 1 is controlled by the engine controller 100, for example, by changing the injection timing of engine 1 or changing the operating state of the turbocharger. Furthermore, blockage of the front of DOC 51 can be detected, for example, based on the temperature difference between the exhaust temperature at the inlet of DOC 51 detected by the DOC inlet temperature sensor 94 during regeneration operation and the exhaust temperature at the outlet of DOC 51 detected by the DOC outlet temperature sensor 95. If the temperature difference is smaller than a predetermined value, it can be detected that the performance of DOC 51 has deteriorated.

[0030] In this embodiment, the regeneration operation and the drying operation are collectively referred to as the performance recovery operation of the engine system 10. The performance recovery operation is an operation to restore the performance of the DPF device 5, which is an example of an aftertreatment device.

[0031] On the other hand, the server 300 is equipped with a processor and has a functional configuration consisting of a combination of hardware such as the processor and software such as a program executed by the processor, comprising an acquisition unit 31, a prediction unit 32, and an operation instruction unit 33. Note that the server 300 is an example of a monitoring device according to this disclosure.

[0032] The acquisition unit 31 periodically acquires operating data from each vehicle 20A to 20D that indicates the operating status of the engine system 10. As described above, the operating data includes, for example, data corresponding to at least one of the engine's fuel consumption, exhaust gas flow rate, and exhaust gas temperature, and data indicating the occurrence of a predetermined event corresponding to a performance degradation of the DPF device 5.

[0033] The prediction unit 32 predicts the performance degradation of the DPF device 5 based on the driving data. The prediction unit 32 predicts the performance degradation of the DPF device 5 using a machine learning model that has been trained using the driving data of the vehicle 20 in which the above event occurred as training data. In this case, the machine learning model uses the driving data as an explanatory variable. The machine learning model can also use, for example, the degree of correlation with the driving data of the vehicle 20 in which the above event occurred as its dependent variable. Alternatively, the dependent variable can be, for example, the degree of probability of the above event occurring. Alternatively, the dependent variable can be, for example, the predicted period until the above event occurs.

[0034] The operation instruction unit 33 instructs the vehicle 20, which has a DPF device 5 that is predicted to experience a predetermined performance degradation, to increase the frequency of performance recovery operation. In the example shown in Figure 1, an operation instruction is transmitted to vehicle 20D.

[0035] As described above, the operation instruction system 500, as explained with reference to Figures 1 and 2, allows for normal performance recovery operation to be performed at an appropriate frequency.

[0036] (First Embodiment) Next, referring to Figures 3 to 8, the operation instruction system 500 will be described in detail, using the case where the performance recovery operation is a dry operation as an example. The configuration of the engine system 10 described with reference to Figure 2 is the same as in this embodiment. The configuration and operation of the engine controller 100, the vehicle controller 200, and the server 300 in this embodiment will be described below. In the server 300 shown in Figure 3, the market operation data acquisition unit 301 corresponds to the acquisition unit 31 shown in Figure 1. The attention vehicle identification unit 305 shown in Figure 3 corresponds to the prediction unit 32 shown in Figure 1. The operation instruction transmission unit 306 shown in Figure 3 corresponds to the operation instruction unit 33 shown in Figure 1. In the operation instruction system 500 shown in Figure 3, the vehicle 20 temporarily stores the operation data in a predetermined cloud 400, and the server 300 acquires the operation data from the cloud 400.

[0037] Figure 3 is a block diagram showing an example of the functional configuration of the operation instruction system 500 according to the embodiment of this disclosure. Figure 4 is a block diagram illustrating a predictive model according to the embodiment of this disclosure. Figures 5 to 7 are schematic diagrams illustrating an example of the configuration of operation data according to the embodiment of this disclosure. Figure 8 is a flowchart showing an example of the operation of the operation instruction system according to the embodiment of this disclosure.

[0038] The server 300 shown in Figure 3 has the following functional configuration: a market operation data acquisition unit 301, a market operation data storage unit 302, a model learning unit 303, a DOC efficiency decline prediction model 304, a vehicle attention identification unit 305, and a driving instruction transmission unit 306. The market operation data acquisition unit 301 acquires driving data for each vehicle 20 and stores it in the market operation data storage unit 302. The market operation data storage unit 302 stores the driving data for each vehicle 20 acquired by the market operation data acquisition unit 301. The model learning unit 303 uses the driving data stored in the market operation data storage unit 302 to machine-learn the DOC efficiency decline prediction model 304. The DOC efficiency decline prediction model 304 is a trained machine learning model and is machine-learned by the model learning unit 303. The vehicle attention identification unit 305 uses the DOC efficiency decline prediction model 304 to predict the efficiency decline of the DOC 51 for each vehicle 20. The operation instruction transmission unit 306 transmits an operation instruction to a vehicle 20 equipped with a DOC 51 that is predicted to experience a predetermined decrease in efficiency, instructing it to increase the frequency of dry operation. The operation instruction transmission unit 306 may, for example, transmit an operation instruction to a vehicle 20 that does not require an increase in the frequency of dry operation, indicating that no such instruction is given.

[0039] Referring to Figure 4, the DOC efficiency decline prediction model 304 and the model learning unit 303 will be explained. As shown in Figure 4, the DOC efficiency decline prediction model 304 is a machine learning model that takes driving data D1 as an explanatory variable and outputs data D2 relating to the correlation between the driving data of the vehicle that generated the fault code and the explanatory variable as an objective variable. The DOC efficiency decline prediction model 304 is a trained model that uses, for example, a neural network as an element, and the weighting coefficients between neurons in each layer of the neural network are optimized by machine learning so that the desired solution is output for a large amount of input data. The DOC efficiency decline prediction model 304 consists of, for example, a program that performs calculations from input to output and a combination of weighting coefficients (parameters) used in those calculations.

[0040] Furthermore, the DOC efficiency reduction prediction model 304 is machine-learned by the model learning unit 303 using the driving data D4 of the vehicle 20 that generated the fault code as training data. In this case, the fault code is a fault code corresponding to DOC regeneration failure, DPF regeneration failure, or DOC blockage abnormality. The driving data D4 includes the driving data at the time the fault code was generated and past driving data of the same vehicle 20. The data D2 output as the target variable can be, for example, the degree of correlation with the driving data of the vehicle 20 that generated the fault code, the degree of probability of generating the fault code, or the predicted period until the fault code is generated. The driving instruction transmission unit 306 transmits a driving instruction to the vehicle 20 that transmitted the driving data to increase the frequency of dry operation if, for example, the degree of correlation with the driving data of the vehicle 20 exceeds a predetermined value, the degree of probability of generating the fault code exceeds a predetermined value, or the predicted period until the fault code is generated falls below a predetermined value.

[0041] The operating data can take the form shown in Figures 5 to 7, for example. Figures 5 to 7 represent an image of the operating data (all values ​​are hypothetical). The operating data D11 shown in Figure 5 represents trend data and includes, for example, the daily maximum, minimum, and average values ​​for each sensor and internal data of the vehicle 20. If a fault code is issued, the code name can be listed on the day the error occurred. The data includes DOC inlet temperature [°C], engine exhaust gas flow rate [g / s], fuel injection amount [mg / str], etc. mg / str is the fuel injection amount per engine stroke. In the example shown in Figure 5, data (1) shows the DOC inlet temperature [°C], and data (2) shows the engine exhaust gas flow rate [g / s].

[0042] The operating data D12 shown in Figure 6 is a one-dimensional time-frequency map. The operating data D12 represents where the sensor values ​​were located on a discretized axis (equally dividing the upper and lower limits of the output), for example, by accumulating counts every 1 second, for example, over a day. The data includes DOC inlet temperature [°C], exhaust gas flow rate [g / s], etc.

[0043] The operating data D13 shown in Figure 7 is a two-dimensional map. The operating data D13 can, for example, represent time frequency. It represents where the engine's operating state was located on the discretized X-axis (engine speed [rpm]) and Y-axis (engine output torque [N·m], fuel injection amount [mg / str], etc.) by accumulating data of counts every 1 second each day. Alternatively, the operating data D13 can represent the average value of the control values. It can be the daily average of sensor values ​​(each temperature [°C], pressure [kPa], etc.) or actuator values ​​(each valve opening [%], etc.) in each cell of the discretized X-axis (engine speed [rpm]) and Y-axis (fuel injection amount [mg / str]). In the operating data D13 shown in Figure 7, the Y-axis represents engine output torque [N·m].

[0044] Returning to Figure 3, the vehicle body controller 200 has a functional configuration that includes a driving instruction receiving unit 201, a driving data transmission unit 202, a drying operation interval calculation unit 203, and a drying operation start and duration instruction unit 204. The engine controller 100 has a functional configuration that includes a drying operation instruction receiving unit 101, a drying operation execution unit 102, and a drying operation execution status transmission unit 103.

[0045] The driving instruction receiving unit 201 receives driving instructions sent by the server 300 to the vehicle 20. The driving data transmission unit 202 periodically transmits predetermined driving data to the server 300 via the cloud 400. The transmission of driving data may be, for example, once a day, or it may be transmitted in near real time. When the drying operation interval calculation unit 203 receives a driving instruction to increase the frequency of drying operations, it calculates the drying operation interval so that the frequency is increased compared to the normal frequency. When it stops receiving driving instructions to increase the frequency of drying operations, or when it receives a driving instruction not to increase the frequency of drying operations, the drying operation interval calculation unit 203 returns the drying operation interval to the normal frequency. The drying operation start and duration instruction unit 204 transmits a drying operation instruction to the engine controller 100 as a drying operation instruction, along with information indicating the duration of the drying operation (for example, an instruction on how many minutes the drying operation should be continued) when the operating interval calculated by the drying operation interval calculation unit 203 has elapsed since the time of the last drying operation.

[0046] Meanwhile, in the engine controller 100, the drying operation instruction receiving unit 101 receives the drying operation instruction transmitted by the drying operation start and duration instruction unit 204. The drying operation execution unit 102 performs the drying operation in accordance with the drying operation instruction received by the drying operation instruction receiving unit 101. The drying operation execution status transmission unit 103 transmits the results of the drying operation to the vehicle controller 200. It is desirable to perform the drying operation, for example, when the engine is started or when the engine is idling. In addition, the drying operation execution status transmission unit 103 transmits information to the vehicle controller 200 indicating when the drying operation is completed or when there is no opportunity to perform the drying operation.

[0047] Next, an example of the operation of the driving instruction system 500 shown in Figure 3 will be explained with reference to Figure 8. The process shown in Figure 8 corresponds to an example of the operation of the server 300 and the vehicle 20 when the server 300 identifies a vehicle 20 to which it will issue a driving instruction. In Figure 8, "Y" means Yes and "N" means No.

[0048] In server 300, the market operation data acquisition unit 301 acquires individual operation data (step S11). Next, the attention vehicle identification unit 305 inputs the operation data into the DOC efficiency reduction prediction model 304 and predicts the possibility of DOC deterioration, etc. (step S12). If DOC deterioration is not predicted (step S13:N), the market operation data acquisition unit 301 acquires the next individual operation data (step S11).

[0049] If DOC deterioration is predicted (step S13:Y), the driving instruction transmission unit 306 transmits a driving instruction to the vehicle 20 in question (step S14).

[0050] In vehicle 20, the vehicle controller 200 receives a driving instruction from the driving instruction receiving unit 201, and the drying operation interval calculation unit 203 calculates the interval for performing the drying operation based on the driving instruction from the server 300 and the operating status of the engine 1. At the same time, the drying operation start and duration instruction unit 204 transmits an instruction to start the drying operation and the duration (step S15).

[0051] Next, in the engine controller 100, the drying operation instruction receiving unit 101 receives an instruction to start the drying operation and the duration, and the drying operation execution unit 102 performs the drying operation based on the instruction from the vehicle body controller 200 (step S16). Next, the drying operation execution status transmission unit 103 transmits to the vehicle body controller 200 that the drying operation has been completed (step S17).

[0052] As described above, in this embodiment, a DOC (diesel oxidation catalyst) is installed in the exhaust of engine 1, and at least one data point corresponding to a fault code related to the DOC, such as the DOC inlet temperature, DOC outlet temperature, exhaust gas flow rate, and engine load factor (combustion consumption), is acquired from the operating vehicle. The correlation between DOC degradation and engine condition is calculated using machine learning, and DOC degradation is predicted.

[0053] Fault codes related to the DOC include, for example, at least one of the following: DOC regeneration failure, DPF regeneration failure, or DOC blockage abnormality.

[0054] Furthermore, the DOC data obtained when fault codes are acquired is used as training data to train a machine learning model.

[0055] Furthermore, machine learning is used to predict the degradation status of the DOC (Domain-Operated Condition) of vehicles operating in the market.

[0056] Furthermore, vehicles that are expected to deteriorate will undergo a short regeneration (drying) operation.

[0057] Furthermore, the regeneration operation may be performed, for example, when the engine is started or while idling.

[0058] According to this embodiment, since the oxidation performance of DOC can be addressed before it deteriorates significantly, prolonged drying operations or stopping operations to perform drying operations in a stable state can be avoided as much as possible. Furthermore, if an operation that leads to deterioration of DOC's oxidation performance is detected in advance, DOC performance can be restored with a simpler countermeasure. In addition, since construction machinery is used in a wide variety of ways, unknown operating methods may lead to deterioration of DOC's oxidation performance, but it may be possible to detect this based on market data.

[0059] (Modification 1 of the first embodiment) The first embodiment detected and addressed signs of PM accumulation in front of the DOC, but for example, the operation instruction system may be configured to detect and address signs of urea deposit formation. In this case, the aftertreatment device includes a urea selective catalytic reduction (urea SCR) system. In this case, the aftertreatment device in the vehicle 20 includes a urea selective catalytic reduction device and an oxidation catalyst. The performance recovery operation is a regeneration operation that increases the temperature of the aftertreatment device by injecting fuel into the exhaust gas. The occurrence of the event corresponds to the generation of a fault code issued when an accumulation of urea-derived deposits exceeding a predetermined amount is detected in the urea selective catalytic reduction device. The operation instruction transmission unit (operation instruction unit) instructs an increase in the frequency of regeneration operation. The accumulation of urea-derived deposits exceeding a predetermined amount in the urea selective catalytic reduction device can be detected, for example, using a NOx (nitrogen oxide) sensor.

[0060] (Modification 2 of the first embodiment) Furthermore, the operation instruction system may be designed to detect and address signs of excessive soot buildup. In the DPF52, the amount of soot buildup can be estimated based on the differential pressure of the DPF. However, at low gas flow rates, the amount of buildup may not be accurately estimated. In such cases, the DPF52 may operate at low gas flow rates, and the amount of soot buildup may become excessive without knowing the exact amount. Therefore, in Modification 2, excessive soot buildup is predicted from the operation method. In this case, the aftertreatment device includes the DPF52 (filter) and the DOC51 (oxidation catalyst), and the performance recovery operation is a regeneration operation that increases the temperature of the aftertreatment device by injecting fuel into the exhaust gas. The occurrence of the event corresponds to the generation of a fault code issued when an accumulation of particulate matter exceeding a predetermined amount is detected on the filter. The operation instruction transmission unit (operation instruction unit) also instructs an increase in the frequency of regeneration operation.

[0061] While embodiments of this invention have been described above with reference to the drawings, the specific configuration is not limited to the embodiments described above, and design modifications and the like are also included within the scope of the gist of this invention. Furthermore, some or all of the program executed by the computer in the above embodiments can be distributed via a computer-readable recording medium or communication line. [Explanation of Symbols]

[0062] 500... Driving instruction system, 10... Engine system, 20... Vehicle (control system), 100... Engine controller (control device), 200... Body controller (control device), 300... Server (monitoring device), 1... Engine, 5... DPF device (after-processing device), 51... DOC, 52... DPF, 31... Acquisition unit, 32... Prediction unit, 33... Driving instruction unit

Claims

1. A plurality of control systems, each having an engine system including an engine and an aftertreatment device for purifying the exhaust gas of the engine, and a control device for automatically controlling a performance recovery operation of the engine system, which is an operation to restore the performance of the aftertreatment device, A monitoring device for remotely monitoring the aforementioned multiple control systems, Equipped with, The aforementioned monitoring device is An acquisition unit that periodically acquires operating data indicating the operating status of each engine system from each of the control systems, A prediction unit that predicts a decrease in the performance of the after-processing device based on the aforementioned operating data, An operation instruction unit that instructs the control system having the after-processing device, which is predicted to experience a predetermined performance degradation, to increase the frequency of the performance recovery operation, Equipped with, The control system generates a fault code that indicates symptoms based on a decrease in the performance of the after-processing device. The aforementioned operating data includes data corresponding to at least one of the engine's fuel consumption, exhaust gas flow rate, and exhaust gas temperature prior to the time the fault code occurred, and the fault code. The prediction unit is a machine learning model that has been trained using the operating data of the control system in which the fault code occurred as training data, The aforementioned machine learning model, The system inputs operating data from the stage prior to the occurrence of the fault code as an explanatory variable, and outputs at least one of the following as an objective variable: the degree of correlation between the input operating data and the operating data of the control system in which the fault code occurred, the degree of probability of the fault code occurring, and the predicted period until the fault code occurs. Driving instruction system.

2. The occurrence of the aforementioned fault code corresponds to a performance degradation of the after-processing device that requires additional action beyond the normal automatic performance recovery operation. The driving instruction system according to claim 1.

3. The post-treatment device includes an oxidation catalyst, The performance recovery operation is a drying operation that dries the oxidation catalyst by raising the temperature of the exhaust gas without injecting fuel into the exhaust gas. The occurrence of the aforementioned fault code corresponds to the occurrence of a fault code issued when a decrease in the performance of the oxidation catalyst is detected. The operation instruction unit instructs an increase in the frequency of the drying operation. The driving instruction system according to claim 1 or 2.

4. The post-treatment device includes a urea selective catalytic reduction device and an oxidation catalyst. The performance recovery operation is a regeneration operation in which fuel is injected into the exhaust gas to raise the temperature of the aftertreatment device. The occurrence of the aforementioned fault code corresponds to the occurrence of a fault code issued when a deposit of urea-derived material exceeding a predetermined amount is detected in the urea selective catalytic reduction device. The operation instruction unit instructs an increase in the frequency of the regeneration operation. The driving instruction system according to claim 1 or 2.

5. The post-treatment device includes a filter and an oxidation catalyst. The performance recovery operation is a regeneration operation in which fuel is injected into the exhaust gas to raise the temperature of the aftertreatment device. The occurrence of the aforementioned fault code corresponds to the occurrence of a fault code issued when an accumulation of particulate matter exceeding a predetermined amount is detected on the filter. The operation instruction unit instructs an increase in the frequency of the regeneration operation. The driving instruction system according to claim 1 or 2.

6. A plurality of control systems, each having an engine system including an engine and an aftertreatment device for purifying the exhaust gas of the engine, and a control device for automatically controlling a performance recovery operation of the engine system, which is an operation to restore the performance of the aftertreatment device, A monitoring device for remotely monitoring the aforementioned multiple control systems, Using In the aforementioned monitoring device, The steps include periodically acquiring operating data indicating the operating status of each engine system from each control system, A step of predicting a decrease in the performance of the after-processing device based on the aforementioned operating data, A step of instructing the control system having the after-processing device, which is predicted to experience a predetermined performance degradation, to increase the frequency of the performance recovery operation; Includes, The control system generates a fault code that indicates symptoms based on a decrease in the performance of the after-processing device. The aforementioned operating data includes data corresponding to at least one of the engine's fuel consumption, exhaust gas flow rate, and exhaust gas temperature prior to the time the fault code occurred, and the fault code. In the aforementioned prediction step, a machine learning model that has been trained using the operating data of the control system in which the fault code occurred as training data is used to predict the performance degradation of the after-processing device in the stage prior to the occurrence of the fault code. The aforementioned machine learning model, The system inputs operating data from the stage prior to the occurrence of the fault code as an explanatory variable, and outputs at least one of the following as an objective variable: the degree of correlation between the input operating data and the operating data of the control system in which the fault code occurred, the degree of probability of the fault code occurring, and the predicted period until the fault code occurs. Instructions for driving.

7. A plurality of control systems, each having an engine system including an engine and an aftertreatment device for purifying the exhaust gas of the engine, and a control device for automatically controlling a performance recovery operation of the engine system, which is an operation to restore the performance of the aftertreatment device, A monitoring device for remotely monitoring the aforementioned multiple control systems, Equipped with, The aforementioned monitoring device is An acquisition unit that periodically acquires operating data indicating the operating status of each engine system from each of the control systems, A prediction unit that predicts a decrease in the performance of the after-processing device based on the aforementioned operating data, An operation instruction unit that instructs the control system having the after-processing device, which is predicted to experience a predetermined performance degradation, to increase the frequency of the performance recovery operation, Equipped with, The aforementioned operating data includes data corresponding to at least one of the engine's fuel consumption, the exhaust gas flow rate, and the exhaust gas temperature, and data indicating the occurrence of a predetermined event corresponding to a performance degradation of the aftertreatment device. The prediction unit predicts the performance degradation of the post-processing device using a machine learning model that has been trained using the operating data of the control system in which the event occurred as training data. The post-treatment device includes an oxidation catalyst, The performance recovery operation is a drying operation that dries the oxidation catalyst by raising the temperature of the exhaust gas without injecting fuel into the exhaust gas. The occurrence of the aforementioned event corresponds to the generation of a fault code issued when a decrease in the performance of the oxidation catalyst is detected. The operation instruction unit instructs an increase in the frequency of the drying operation. Driving instruction system.

8. A plurality of control systems, each having an engine system including an engine and an aftertreatment device for purifying the exhaust gas of the engine, and a control device for automatically controlling a performance recovery operation of the engine system, which is an operation to restore the performance of the aftertreatment device, A monitoring device for remotely monitoring the aforementioned multiple control systems, Equipped with, The aforementioned monitoring device is An acquisition unit that periodically acquires operating data indicating the operating status of each engine system from each of the control systems, A prediction unit that predicts a decrease in the performance of the after-processing device based on the aforementioned operating data, An operation instruction unit that instructs the control system having the after-processing device, which is predicted to experience a predetermined performance degradation, to increase the frequency of the performance recovery operation, Equipped with, The aforementioned operating data includes data corresponding to at least one of the engine's fuel consumption, the exhaust gas flow rate, and the exhaust gas temperature, and data indicating the occurrence of a predetermined event corresponding to a performance degradation of the aftertreatment device. The prediction unit predicts the performance degradation of the post-processing device using a machine learning model that has been trained using the operating data of the control system in which the event occurred as training data. The post-treatment device includes a urea selective catalytic reduction device and an oxidation catalyst. The performance recovery operation is a regeneration operation in which fuel is injected into the exhaust gas to raise the temperature of the aftertreatment device. The occurrence of the aforementioned event corresponds to the generation of a fault code issued when a deposit of urea-derived material exceeding a predetermined amount is detected in the urea selective catalytic reduction device. The operation instruction unit instructs an increase in the frequency of the regeneration operation. Driving instruction system.

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