Parameter correction method, device and electronic equipment for natural gas engine

Abstract

The present application provides a method, device and electronic device for correcting parameters of a natural gas engine. The method includes: obtaining a first gas quality factor self-learning value by obtaining a gas quality factor self-learning value obtained in the last cycle of the vehicle; obtaining the historical gas quality factor self-learning values ​​of all online vehicles that refuel at the gas station closest to the vehicle; controlling the vehicle's gas quality factor self-learning process in this driving cycle according to the historical gas quality factor self-learning value and the first gas quality factor self-learning value to obtain a second gas quality factor self-learning value in this driving cycle, and correcting at least the engine performance parameter according to the second gas quality factor self-learning value. The present application performs a gas quality factor self-learning process for the vehicle in this driving cycle according to the gas quality factor self-learning value obtained in the last cycle and the historical gas quality factor self-learning value, thereby ensuring the accuracy of the correction of the engine performance parameters.

Description

Technical Field

[0001] The present application relates to the field of engine technology, and in particular to a parameter correction method for a natural gas engine, a parameter correction device for a natural gas engine, and an electronic device. Background Art

[0002] The fuels generally used in natural gas engines are LNG (liquefied natural gas) and CNG (compressed natural gas). In order to meet the needs of long-distance transportation, most of them use LNG as fuel, and transportation vehicles need to be refueled with natural gas in different regions.

[0003] At present, there are differences in the gas quality composition and calorific value of LNG produced in different regions on the market. If the same engine control data is used, it will lead to problems such as engine overpower and detonation under high calorific value gas, and insufficient engine power and misfire under low calorific value gas. In view of the above differences, the related technology triggers the gas quality self-learning function after each fuel filling, and corrects the engine-related data based on the gas quality factor. This self-learning solution can only realize the correction of engine performance indicators within a certain range, and the correction accuracy is low. Summary of the invention

[0004] The main purpose of the present application is to provide a parameter correction method for a natural gas engine, a parameter correction device for a natural gas engine and an electronic device, so as to at least solve the technical problem in the related art that the gas quality factor self-learning scheme has a low accuracy rate in correcting engine-related data.

[0005] To achieve the above-mentioned purpose, according to one aspect of the present application, a parameter correction method for a natural gas engine is provided, wherein the natural gas engine is installed on a vehicle, and comprises: obtaining a first gas quality factor self-learning value obtained by the vehicle in a previous driving cycle, and a fuel filling state of the vehicle in this driving cycle, wherein a driving cycle is a driving process from ECU power-on to ECU power-off, and the fuel filling state is used to indicate whether fuel is being added to the vehicle; determining whether the fuel filling state is set, and obtaining the location information of the vehicle when the fuel filling state is set; determining the gas station closest to the vehicle and the historical gas quality factor self-learning values ​​of all online vehicles that refueled at the gas station according to the location information of the vehicle; controlling the gas quality factor self-learning process of the vehicle in the current driving cycle according to the historical gas quality factor self-learning value and the first gas quality factor self-learning value to obtain a second gas quality factor self-learning value in the current driving cycle, and correcting at least the engine performance parameter according to the second gas quality factor self-learning value.

[0006] Optionally, controlling the gas quality factor self-learning process of the vehicle in the current driving cycle according to the historical gas quality factor self-learning value and the first gas quality factor self-learning value to obtain a second gas quality factor self-learning value in the current driving cycle includes: obtaining the absolute value of the difference between the historical gas quality factor self-learning value and the first gas quality factor self-learning value; when the absolute value of the difference is greater than a preset difference, determining the historical gas quality factor self-learning value as the gas quality factor self-learning initial value of the gas quality factor self-learning process in the current driving cycle; when the absolute value of the difference is less than or equal to the preset difference, determining a weighted value of the historical gas quality factor self-learning value and the first gas quality factor self-learning value as the gas quality factor self-learning initial value of the gas quality factor self-learning process in the current driving cycle; and performing the gas quality factor self-learning process of the vehicle in the current driving cycle according to the gas quality factor self-learning initial value to obtain the second gas quality factor self-learning value.

[0007] Optionally, correcting the engine performance parameters according to the second gas quality factor self-learning value includes: determining whether the second gas quality factor self-learning value is within a first preset range; when the second gas quality factor self-learning value is within the first preset range, using the second gas quality factor self-learning value to correct the EGR rate, ignition advance angle, gas injection amount and air amount of the engine; when the second gas quality factor self-learning value is not within the first preset range, using the second gas quality factor self-learning value to adjust the gas injection amount and the air amount, and updating the EGR rate MAP and the ignition advance angle MAP based on the second gas quality factor self-learning value.

[0008] Optionally, when the second gas quality factor self-learning value is within the first preset range, after using the second gas quality factor self-learning value to correct the EGR rate, ignition advance angle, gas injection amount and air amount of the engine, the method further includes: determining the ratio of the exhaust gas amount entering the cylinder of the engine to the total intake air sucked into the cylinder according to the corrected EGR rate; controlling the engine ignition according to the corrected ignition advance angle; controlling the injection valve of the engine to inject gas with a volume equal to the corrected gas injection amount; and controlling the cylinder of the engine to inhale air with a volume equal to the corrected air amount.

[0009] Optionally, the method also includes: obtaining the historical gas quality factor self-learning value; wherein, the step of obtaining the historical gas quality factor self-learning value includes: obtaining the historical self-learning value of each of the online vehicles that refuel at the gas station; removing abnormal values ​​from a plurality of the historical self-learning values ​​to obtain a plurality of non-abnormal historical self-learning values; and determining a weighted value of the plurality of the non-abnormal historical self-learning values ​​as the historical gas quality factor self-learning value.

[0010] Optionally, before removing abnormal values ​​from the multiple historical self-learning values ​​to obtain multiple non-abnormal historical self-learning values, the method also includes: determining whether each of the historical self-learning values ​​is within a second preset range; and determining the historical self-learning values ​​that are not within the second preset range as the abnormal values.

[0011] Optionally, determining whether the fuel filling status is set includes: obtaining setting identification information, wherein when the setting identification information is a first value, it indicates that the fuel filling status is set, and when the setting identification information is a second value, it indicates that the fuel filling status is not set; and determining whether the fuel filling status is set according to the setting identification information.

[0012] Optionally, determining the gas station closest to the vehicle based on the location information of the vehicle includes: downloading a navigation map from the cloud; locating the vehicle in the navigation map based on the location information of the vehicle to obtain a positioning result; and determining the gas station closest to the vehicle in the navigation map based on the positioning result.

[0013] According to another aspect of the present application, a parameter correction device for a natural gas engine is provided, the natural gas engine being installed on a vehicle, comprising: an acquisition unit, for acquiring a first gas quality factor self-learning value obtained by the vehicle in a previous driving cycle, and a fuel filling state of the vehicle in this driving cycle, wherein one driving cycle is a driving process from ECU power-on to ECU power-off, and the fuel filling state is used to indicate whether fuel is being added to the vehicle; a first determination unit, for determining whether the fuel filling state is set, and acquiring the position information of the vehicle when the fuel filling state is set; a second determination unit, for determining the gas station closest to the vehicle and the historical gas quality factor self-learning values ​​of all online vehicles that refuel at the gas station according to the position information of the vehicle; and a control unit, for controlling the gas quality factor self-learning process of the vehicle in the current driving cycle according to the historical gas quality factor self-learning value and the first gas quality factor self-learning value, so as to obtain a second gas quality factor self-learning value in the current driving cycle, and at least correct the engine performance parameter according to the second gas quality factor self-learning value.

[0014] According to another aspect of the present application, an electronic device is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a parameter correction method for executing any one of the natural gas engines described.

[0015] By applying the technical solution of the present application, firstly, a first gas quality factor self-learning value obtained by the vehicle in the previous driving cycle and a fuel filling state of the vehicle in this driving cycle are obtained, wherein one driving cycle is a driving process from ECU power-on to ECU power-off, and the fuel filling state is used to indicate whether fuel is being added to the vehicle; then, it is determined whether the fuel filling state is set, and when the fuel filling state is set, the position information of the vehicle is obtained; then, the gas station closest to the vehicle and the historical gas quality factor self-learning values ​​of all online vehicles that refuel at the gas station are determined according to the position information of the vehicle; finally, the gas quality factor self-learning process of the vehicle in this driving cycle is controlled according to the historical gas quality factor self-learning value and the first gas quality factor self-learning value to obtain a second gas quality factor self-learning value in this driving cycle, and at least the engine performance parameter is corrected according to the second gas quality factor self-learning value. The present application combines the gas quality factor self-learning value obtained in the previous cycle with the gas quality factor self-learning value learned historically to perform the current gas quality factor self-learning process, thereby ensuring the accuracy of the engine performance parameter correction, and solving the technical problem of low accuracy of the gas quality factor self-learning scheme in the related art in correcting engine-related data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings constituting part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 A hardware structure block diagram of a mobile terminal for executing a parameter correction method for a natural gas engine provided in an embodiment of the present application is shown;

[0018] Figure 2 A schematic flow chart of a method for correcting parameters of a natural gas engine provided according to an embodiment of the present application is shown;

[0019] Figure 3 A schematic diagram of a process for obtaining a second gas quality factor self-learning value according to an embodiment of the present application is shown;

[0020] Figure 4A schematic diagram of a process for correcting engine performance parameters according to a second gas quality factor self-learning value provided in accordance with an embodiment of the present application is shown;

[0021] Figure 5 A schematic flow chart of a specific method for correcting parameters of a natural gas engine provided according to an embodiment of the present application is shown;

[0022] Figure 6 A structural block diagram of a parameter correction device for a natural gas engine provided according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0023] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0024] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] For the convenience of description, some nouns or terms involved in the embodiments of the present application are explained below:

[0027] Gas quality factor self-learning value: The gas quality in different regions varies (composition, calorific value, etc.). After the vehicle gas tank is filled with new natural gas, it will generally trigger the gas quality factor self-learning, and finally output the gas quality factor self-learning value, which is used to correct the gas injection amount, air amount and EGR to ensure the performance indicators of the engine. The self-learning value is stored in the EEPROM when the ECU is powered off, and will be updated after self-learning again after the next gas filling.

[0028] EGR (Exhaust Gas Re-Circulation): Part of the exhaust gas discharged by the engine is returned to the intake manifold and sent back into the cylinder together with the fresh mixture.

[0029] OTA (Over-the-air technology) is a technology that transmits the program or data that needs to be upgraded of the ECU directly to the vehicle communication terminal (Tbox) through the network, and writes it to the ECU through the vehicle communication terminal to achieve remote upgrade.

[0030] MAP (map): Input X, Y, and output the corresponding value Z.

[0031] As introduced in the background technology, in the prior art, the gas quality self-learning function is triggered after each fuel filling, and the engine-related data is corrected based on the gas quality factor. This self-learning scheme can only achieve the correction of engine performance indicators within a certain range, and the correction accuracy is low. In order to solve the technical problem of low accuracy in correcting engine-related data by the gas quality factor self-learning scheme in the related technology, the embodiments of the present application provide a parameter correction method for a natural gas engine, a parameter correction device for a natural gas engine and an electronic device.

[0032] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0033] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a mobile terminal of a method for correcting parameters of a natural gas engine according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0034] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the display method of device information in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0035] In this embodiment, a method for correcting parameters of a natural gas engine running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0036] Figure 2 FIG. 1 is a flow chart of a method for correcting parameters of a natural gas engine according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0037] Step S201, obtaining a first fuel gas quality factor self-learning value obtained by the vehicle in a previous driving cycle, and a fuel filling state of the vehicle in this driving cycle;

[0038] Among them, one driving cycle is a driving process from ECU power-on to ECU power-off, and the fuel filling status is used to indicate whether fuel is being added to the vehicle.

[0039] Specifically, by obtaining the first gas quality factor self-learning value obtained by the vehicle in the previous cycle, the gas quality factor self-learning value in this driving cycle can be predicted more accurately. The fuel filling status is the filling status (set) and the non-filling status. The first gas quality factor self-learning value is the gas quality factor self-learning value finally output by the gas quality factor self-learning function after the vehicle gas tank was triggered by the last new natural gas filling.

[0040] Step S202, determining whether the fuel filling state is set, and if the fuel filling state is set, obtaining the position information of the vehicle;

[0041] Specifically, when the fuel is being refueled, the latitude and longitude (position) information of the vehicle is confirmed based on the positioning module of the vehicle-mounted communication terminal (Tbox) installed on the vehicle.

[0042] Step S203, determining the gas station closest to the vehicle and the historical gas quality factor self-learning values ​​of all online vehicles that refuel at the gas station according to the location information of the vehicle;

[0043] Specifically, the surrounding gas stations are confirmed according to the vehicle's positioning information, and the gas station closest to the vehicle is determined, so as to obtain the historical gas quality factor self-learning values ​​of all online vehicles that refueled at the gas station. The gas quality self-learning conditions of all online vehicles after refueling at the gas station can be comprehensively considered, and the gas quality factor self-learning value in this driving cycle can be more accurately predicted.

[0044] Step S204, controlling the vehicle's gas quality factor self-learning process in the current driving cycle according to the historical gas quality factor self-learning value and the first gas quality factor self-learning value to obtain a second gas quality factor self-learning value in the current driving cycle, and correcting at least the engine performance parameter according to the second gas quality factor self-learning value.

[0045] Specifically, the second gas quality factor self-learning value in this driving cycle is obtained according to the historical gas quality factor self-learning value and the first gas quality factor self-learning value. The second gas quality factor self-learning value is the gas quality factor self-learning function triggered after the vehicle's gas tank is filled with new natural gas. The gas quality factor self-learning value is finally output, which is used to correct the gas injection amount, air amount and EGR, so as to ensure the performance indicators of the engine, thereby realizing real-time correction and optimization of engine parameters, improving the adaptability of the engine, and better adapting to the quality of various gases, so as to achieve more stable and efficient performance.

[0046] An embodiment of the present application provides a parameter correction method for a natural gas engine, which first obtains a first gas quality factor self-learning value obtained by a vehicle in a previous driving cycle, and a fuel filling status of the vehicle in this driving cycle, wherein a driving cycle is a driving process from ECU power-on to ECU power-off, and the fuel filling status is used to indicate whether fuel is being added to the vehicle; then determines whether the fuel filling status is set, and when the fuel filling status is set, obtains the location information of the vehicle; then determines the gas station closest to the vehicle and the historical gas quality factor self-learning values ​​of all online vehicles that refuel at the gas station according to the location information of the vehicle; finally, controls the gas quality factor self-learning process of the vehicle in this driving cycle according to the historical gas quality factor self-learning value and the first gas quality factor self-learning value to obtain a second gas quality factor self-learning value in this driving cycle, and corrects at least the engine performance parameter according to the second gas quality factor self-learning value. In the embodiment of the present application, the self-learning process of the current gas quality factor is performed in combination with the self-learning value of the gas quality factor obtained in the previous cycle and the self-learning value of the gas quality factor learned historically, so as to ensure the accuracy of the correction of the engine performance parameters, and solve the technical problem that the gas quality factor self-learning scheme in the related art has a low accuracy rate in correcting the engine related data.

[0047] In the prior art, the gas quality self-learning function is triggered after each fuel filling of the vehicle, and the engine-related data is corrected based on the gas quality factor of the current fuel filling, which can only realize the correction of engine performance indicators within a certain range, and lacks verification of the rationality of the self-learning value. The accuracy of this correction method is relatively low. The parameter correction method of the natural gas engine of the present application can avoid the above phenomenon, and the accuracy of the correction of the engine performance indicators is higher.

[0048] As a possible implementation, Figure 3 As shown, the gas quality factor self-learning process of the vehicle in this driving cycle is controlled according to the historical gas quality factor self-learning value and the first gas quality factor self-learning value to obtain the second gas quality factor self-learning value in this driving cycle, including the following steps:

[0049] Step S301, obtaining the absolute value of the difference between the historical gas quality factor self-learning value and the first gas quality factor self-learning value;

[0050] Specifically, the historical gas quality factor self-learning value is obtained through weighted calculation. Before weighting the historical gas quality factor self-learning values ​​of all online vehicles at the gas station, it is necessary to clean up the abnormal values, and then compare the historical gas quality factor self-learning value with the first gas quality factor self-learning value to obtain the absolute value of the difference between the two.

[0051] Step S302, when the absolute value of the difference is greater than the preset difference, the historical gas quality factor self-learning value is determined as the gas quality factor self-learning initial value of the gas quality factor self-learning process in the current driving cycle;

[0052] Specifically, the normal range of the historical gas quality factor self-learning value and the first gas quality factor self-learning value is between 0.8 and 1.3, and the preset difference is a calibration value, which can be freely calibrated according to actual usage. The conventional preset difference can be 0.4. When the absolute value of the difference is greater than the preset difference, it indicates that the difference between the historical gas quality factor self-learning value and the first gas quality factor self-learning value is too large, indicating that the quality of the fuel added by the gas station is different from the fuel last added by the vehicle. In order to prevent the vehicle from running abnormally with the newly added fuel, the initial value of the quality factor self-learning value is set to the historical gas quality factor self-learning value before the new gas quality factor self-learning is performed.

[0053] Step S303, when the absolute value of the difference is less than or equal to the preset difference, a weighted value of the historical gas quality factor self-learning value and the first gas quality factor self-learning value is determined as the gas quality factor self-learning initial value of the gas quality factor self-learning process in the current driving cycle;

[0054] Specifically, when the absolute value of the difference is less than or equal to the preset difference, it indicates that the difference between the historical gas quality factor self-learning value and the first gas quality factor self-learning value is not much, and before the vehicle performs a new gas quality factor self-learning, the initial value of the quality factor self-learning value is set to the weighted value of the historical gas quality factor self-learning value and the first gas quality factor self-learning value. The weights of the historical gas quality factor self-learning value and the historical gas quality factor self-learning value in the weighted calculation can be freely calibrated.

[0055] Step S304 , performing a gas quality factor self-learning process for the vehicle in this driving cycle according to the gas quality factor self-learning initial value, and obtaining a second gas quality factor self-learning value.

[0056] Specifically, when the vehicle engine operating condition meets the activation condition of the gas quality factor self-learning function, the second gas quality factor self-learning value after the current refueling is learned again based on the gas quality factor self-learning initial value, and the gas quality factor self-learning process of the vehicle in the current driving cycle is controlled by acquiring the historical gas quality factor self-learning value and the first gas quality factor self-learning value in real time to obtain the second gas quality factor self-learning value, thereby realizing real-time correction and optimization of engine parameters, improving the driving safety and stability of the vehicle, and reducing the incidence of traffic accidents.

[0057] It can be seen that before the engine controller has completed the self-learning value of the quality factor of the newly added fuel, based on the historical big data summary and analysis of other similar vehicles after refueling at the gas station, combined with the gas quality factor learned by the engine controller before refueling, a reasonable initial value of the self-learning value of the gas quality factor after refueling is given, which prevents the abnormal engine performance index caused by the large difference in fuel quality between the two refuelings. That is, by comparing the absolute value of the historical gas quality factor self-learning value and the first gas quality factor self-learning value, the gas quality factor self-learning process of the vehicle in this driving cycle is adjusted to obtain the second gas quality factor self-learning value, so that the change of the gas quality factor can be more accurately predicted, the engine parameters can be corrected and optimized in real time, and the engine performance can be improved.

[0058] As a possible implementation, Figure 4 As shown, correcting the engine performance parameters according to the second fuel gas quality factor self-learning value includes the following steps:

[0059] Step S401, determining whether the second gas quality factor self-learning value is within a first preset range;

[0060] Specifically, after the gas quality factor self-learning value after this refueling is learned based on the gas quality factor self-learning initial value to obtain the second gas quality factor self-learning value, it is necessary to verify the second gas quality factor self-learning value to determine whether the second gas quality factor self-learning value is within a first preset range. In an embodiment of the present application, the upper limit of the first preset range is set to 1.2 and the lower limit is set to 0.8.

[0061] Step S402, when the second gas quality factor self-learning value is within the first preset range, the second gas quality factor self-learning value is used to correct the EGR rate, ignition advance angle, gas injection amount and air amount of the engine;

[0062] Specifically, when the second gas quality factor self-learning value is within the first preset range, it means that the gas quality of this filling is within the reasonable range of conventional settings, and the conventional scheme can be used to correct the relevant parameters of the engine. The conventional scheme is: using the second gas quality factor self-learning value to correct the engine's EGR rate, ignition advance angle, gas injection amount and air amount.

[0063] Step S403, when the second gas quality factor self-learning value is not within the first preset range, the second gas quality factor self-learning value is used to adjust the gas injection amount and the air amount, and the EGR rate MAP and the ignition advance angle MAP are updated based on the second gas quality factor self-learning value.

[0064] Specifically, when the second gas quality factor self-learning value is not within the first preset range, it means that the quality of the gas filled this time exceeds the conventional setting range. Correcting the relevant parameters of the engine through conventional solutions can no longer well guarantee the relevant performance indicators of the engine. It is necessary to modify and upgrade the data MAP related to the gas quality factor self-learning value and affecting the engine performance indicators according to the second gas quality factor self-learning value. The specific method can be to automatically obtain and send down the data related to the engine performance indicators for upgrading after checking the historical experience data stored in the new cloud platform through the second gas quality factor self-learning value, or it can be that after the Tbox sends back the second gas quality factor self-learning value, the relevant personnel reset the above MAP data and then send down the data related to the engine performance indicators for remote automatic upgrade.

[0065] It can be seen from this that after the engine controller completes the self-learning value of the gas quality factor of the newly added fuel, the rationality of the newly learned quality factor self-learning of the vehicle's engine controller is checked in combination with the historical fuel quality factor self-learning values ​​of other similar vehicles after refueling at the gas station. When it exceeds the preset range, the gas injection amount and air amount are corrected while modifying the engine performance data such as the EGR rate demand basic MAP and the ignition advance angle basic MAP, and the data is upgraded through OTA to achieve optimal control of the engine performance indicators.

[0066] As a possible implementation manner, when the second gas quality factor self-learning value is within a first preset range, after the second gas quality factor self-learning value is used to correct the EGR rate, ignition advance angle, gas injection amount and air amount of the engine, the method also includes: determining the ratio of the exhaust gas amount entering the cylinder of the engine to the total intake air sucked into the cylinder according to the corrected EGR rate; controlling the engine ignition according to the corrected ignition advance angle; controlling the engine injection valve to inject gas with a volume equal to the corrected gas injection amount; and controlling the engine cylinder to inhale air with a volume equal to the corrected air amount.

[0067] It can be seen that by adjusting the engine's EGR rate, ignition advance angle, gas injection amount, air volume and other parameters in real time according to the second gas quality factor self-learning value, it is possible to more accurately adapt to changes in gas quality, improve engine combustion efficiency, reduce fuel consumption, and thus reduce the owner's fuel expenses. By making multiple judgments based on the second gas quality factor self-learning value to decide how to adjust parameters such as gas injection amount and air volume, the system's intelligence is enhanced, and the system's adaptability and stability are improved.

[0068] As a possible implementation manner, the method also includes: obtaining historical gas quality factor self-learning values; wherein the step of obtaining historical gas quality factor self-learning values ​​includes: obtaining historical self-learning values ​​of each online vehicle that refuels at a gas station; removing abnormal values ​​from multiple historical self-learning values ​​to obtain multiple non-abnormal historical self-learning values; and determining a weighted value of multiple non-abnormal historical self-learning values ​​as the historical gas quality factor self-learning value.

[0069] It can be seen that by obtaining the historical self-learning values ​​of each online vehicle refueling at the gas station and removing abnormal values, multiple non-abnormal historical self-learning values ​​are obtained, and then the historical gas quality factor self-learning values ​​are calculated, the validity and reliability of the historical data can be improved.

[0070] As a possible implementation manner, before removing abnormal values ​​from a plurality of historical self-learning values ​​to obtain a plurality of non-abnormal historical self-learning values, the method further includes:

[0071] Step S501, determining whether each historical self-learning value is within a second preset range;

[0072] Specifically, by determining whether each historical self-learning value is within the second preset range, historical self-learning values ​​that meet the requirements can be accurately screened out, thereby improving the validity and reliability of historical data.

[0073] Step S502: determine the historical self-learning value that is not within the second preset range as an abnormal value.

[0074] It can be seen that by calculating the historical gas quality factor self-learning value based on the screened historical self-learning value, and adjusting the engine's EGR rate, ignition advance angle, gas injection amount, air volume and other parameters, it is possible to more accurately adapt to changes in gas quality and improve the engine's adaptability and stability.

[0075] As a possible implementation manner, determining whether the fuel filling state is set includes:

[0076] Step S601, obtaining setting identification information, wherein when the setting identification information is a first value, it indicates that the fuel filling state is set, and when the setting identification information is a second value, it indicates that the fuel filling state is not set;

[0077] Specifically, by acquiring the setting identification information and its corresponding value, it is possible to accurately determine whether the fuel filling status is set, thereby avoiding misjudgment or missed judgment, and improving the accuracy and reliability of the system.

[0078] Step S602, determining whether the fuel filling state is set according to the setting identification information.

[0079] Specifically, by determining whether the fuel filling state is set, it is possible to accurately determine which data in the historical data set is related to filling, thereby improving the reliability and effectiveness of the historical data. Therefore, according to the setting of the fuel filling state, the engine's EGR rate, ignition advance angle, gas injection amount, air volume and other parameters can be adjusted, which can more accurately adapt to fuel changes and improve the adaptability and stability of the engine.

[0080] As a possible implementation method, determining the gas station closest to the vehicle based on the vehicle's location information includes: downloading a navigation map from the cloud; locating the vehicle in the navigation map based on the vehicle's location information to obtain a positioning result; and determining the gas station closest to the vehicle in the navigation map based on the positioning result.

[0081] Specifically, by locating the vehicle in the navigation map according to the vehicle's location information and determining the gas station closest to the vehicle, the gas station closest to the vehicle can be accurately determined, avoiding the vehicle missing the nearest gas station due to inaccurate location information, thereby improving the user experience.

[0082] It can be seen that by downloading the navigation map, locating the vehicle and determining the nearest gas station, the nearest gas station can be accurately determined, the gas filling efficiency can be improved, the operating costs can be reduced, the safety and reliability of the vehicle can be enhanced, and the intelligence of the system can be enhanced.

[0083] In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the implementation process of the parameter correction method of the natural gas engine of the present application will be described in detail below in combination with specific embodiments.

[0084] This embodiment relates to a specific parameter correction method for a natural gas engine, such as Figure 5 As shown, the following steps are included:

[0085] Step S1: when the ECU is powered on, the self-learning value B of the gas quality factor stored in the last driving cycle of the vehicle, the fuel filling status of the vehicle in the current driving cycle and other parameters are obtained from the EEP, wherein the self-learning value B of the gas quality factor is the first self-learning value of the gas quality factor;

[0086] Step S2: Determine whether the fuel filling status is set. If so, confirm the location (latitude and longitude) of the vehicle based on the Tbox positioning module on the vehicle. If not, execute step S1;

[0087] Step S3: After confirming the location (latitude and longitude) of the vehicle based on the Tbox positioning module on the vehicle, confirm the surrounding gas station information and the relevant historical data of all online vehicles that refueled at the gas station according to the vehicle location information;

[0088] Step S4: Comprehensively consider the self-learning of gas quality of all online vehicles after filling gas at the gas station, and calculate the gas quality factor A of the gas station by weight after removing abnormal values, wherein the gas quality factor A is the self-learning value of the historical gas quality factor;

[0089] Step S5: Determine whether the absolute value of the difference between the self-learning values ​​A and B of the gas quality factor is greater than a preset threshold value 1. If so, set the initial value of the gas quality factor of the vehicle in this driving cycle to be equal to A. If not, set the initial value of the gas quality factor of the vehicle to be equal to the weighted value of A and B (A*fac1+B*fac2), wherein the preset threshold value 1 is the preset difference, fac1 is the weight value of the self-learning value A of the gas quality factor, and fac2 is the weight value of the self-learning value B of the gas quality factor;

[0090] Step S6: After the initial value of the gas quality factor is set, the gas quality self-learning function is activated after the engine operating condition is met, and the gas quality factor self-learning value C after the current refueling is re-learned based on the initial value, wherein the gas quality factor self-learning value C is the second gas quality factor self-learning value;

[0091] Step S7: Determine whether the self-learning value C is within the preset range. If so, execute the conventional plan: correct the EGR rate, ignition advance angle, gas injection amount and air amount in real time through the quality factor self-learning value C. If not, execute the new plan: adjust the gas injection amount and air amount through the quality factor self-learning value, and modify the EGR rate requirement basic MAP, ignition advance angle basic MAP and other data based on the quality factor self-learning value C, and upgrade the data through OTA.

[0092] In an embodiment of the present application, before the engine controller has completed the self-learning value of the quality factor of the newly added fuel, based on the historical big data summary and analysis after the refueling of other similar vehicles at the gas station, combined with the gas quality factor learned by the engine controller before the new fuel is added, a reasonable initial value of the self-learning value of the gas quality factor after the new fuel is added is given, thereby preventing the abnormal engine performance indicator problem caused by the large difference in fuel quality between the two refuelings.

[0093] Furthermore, after the engine controller completes the self-learning value of the quality factor of the newly added fuel, the rationality of the newly learned quality factor self-learning of the vehicle's engine controller is checked in combination with the historical fuel quality factor self-learning values ​​of other similar vehicles after refueling at the gas station. When it exceeds the preset range, the engine performance data such as the EGR rate demand basic MAP and the ignition advance angle basic MAP are modified while correcting the gas injection amount and the air amount, and the data is upgraded through OTA to achieve optimal control of the engine performance indicators.

[0094] It can be seen that by adjusting the historical gas quality factor self-learning value and the first gas quality factor self-learning value, the gas quality factor self-learning process of the vehicle in this driving cycle is adjusted to obtain the second gas quality factor self-learning value, so that the change of the gas quality factor can be predicted more accurately, the engine parameters can be corrected and optimized in real time, and the performance of the natural gas engine can be improved.

[0095] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0096] The embodiment of the present application also provides a parameter correction device for a natural gas engine. It should be noted that the parameter correction device for a natural gas engine in the embodiment of the present application can be used to execute the parameter correction method for a natural gas engine provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0097] The following is an introduction to the parameter correction device for the natural gas engine provided in the embodiment of the present application.

[0098] Figure 6 Schematic diagram of a parameter correction device for a natural gas engine according to an embodiment of the present application. Figure 6 As shown, the device includes: an acquisition unit 61, a first determination unit 62, a second determination unit 63 and a control unit 64.

[0099] an acquisition unit 61, used to acquire a first gas quality factor self-learning value obtained by the vehicle in a previous driving cycle, and a fuel filling state of the vehicle in this driving cycle, wherein one driving cycle is a driving process from ECU power-on to ECU power-off, and the fuel filling state is used to indicate whether fuel is being added to the vehicle;

[0100] Specifically, by obtaining the first gas quality factor self-learning value obtained by the vehicle in the previous cycle, the gas quality factor self-learning value in this driving cycle can be predicted more accurately. The fuel filling status is the filling status (set) and the non-filling status. The first gas quality factor self-learning value is the gas quality factor self-learning value finally output by the gas quality factor self-learning function after the vehicle gas tank was triggered by the last new natural gas filling.

[0101] A first determining unit 62, configured to determine whether the fuel filling state is set, and to obtain the position information of the vehicle if the fuel filling state is set;

[0102] Specifically, when the fuel is being refueled, the latitude and longitude (position) information of the vehicle is confirmed based on the positioning module of the vehicle-mounted communication terminal (Tbox) installed on the vehicle.

[0103] A second determining unit 63 is used to determine the gas station closest to the vehicle and the historical gas quality factor self-learning values ​​of all online vehicles that refuel at the gas station according to the location information of the vehicle;

[0104] Specifically, the surrounding gas stations are confirmed according to the vehicle's positioning information, and the gas station closest to the vehicle is determined, so as to obtain the historical gas quality factor self-learning values ​​of all online vehicles that refueled at the gas station. The gas quality self-learning conditions of all online vehicles after refueling at the gas station can be comprehensively considered, and the gas quality factor self-learning value in this driving cycle can be more accurately predicted.

[0105] The control unit 64 is used to control the gas quality factor self-learning process of the vehicle in the current driving cycle according to the historical gas quality factor self-learning value and the first gas quality factor self-learning value to obtain the second gas quality factor self-learning value in the current driving cycle, and at least correct the engine performance parameter according to the second gas quality factor self-learning value.

[0106] Specifically, the second gas quality factor self-learning value in this driving cycle is obtained according to the historical gas quality factor self-learning value and the first gas quality factor self-learning value. The second gas quality factor self-learning value is the gas quality factor self-learning function triggered after the vehicle's gas tank is filled with new natural gas. The gas quality factor self-learning value is finally output, which is used to correct the gas injection amount, air amount and EGR, so as to ensure the performance indicators of the engine, thereby realizing real-time correction and optimization of engine parameters, improving the adaptability of the engine, and better adapting to the quality of various gases, so as to achieve more stable and efficient performance.

[0107] An embodiment of the present application provides a parameter correction device for a natural gas engine, the device comprising: an acquisition unit, a first determination unit, a second determination unit and a control unit, the acquisition unit being used to acquire a first gas quality factor self-learning value obtained by a vehicle in a previous driving cycle, and a fuel filling state of the vehicle in this driving cycle, wherein one driving cycle is a driving process from ECU power-on to ECU power-off, and the fuel filling state is used to indicate whether fuel is being added to the vehicle; the first determination unit is used to determine whether the fuel filling state is set, and when the fuel filling state is set, acquire the location information of the vehicle; the second determination unit is used to determine the gas station closest to the vehicle and the historical gas quality factor self-learning values ​​of all online vehicles that refuel at the gas station according to the location information of the vehicle; the control unit is used to control the gas quality factor self-learning process of the vehicle in this driving cycle according to the historical gas quality factor self-learning value and the first gas quality factor self-learning value, so as to obtain the second gas quality factor self-learning value in this driving cycle, and at least correct the engine performance parameter according to the second gas quality factor self-learning value. The device combines the gas quality factor self-learning value obtained in the previous cycle with the gas quality factor self-learning value learned historically to perform the current gas quality factor self-learning process, thereby ensuring the accuracy of engine performance parameter correction and solving the technical problem of low accuracy of gas quality factor self-learning scheme in related technologies in correcting engine-related data.

[0108] As a possible implementation manner, the control unit includes: a difference acquisition module, a first initial value determination module, a second initial value determination module and an acquisition module.

[0109] The difference acquisition module is used to obtain the absolute value of the difference between the historical gas quality factor self-learning value and the first gas quality factor self-learning value;

[0110] Specifically, the historical gas quality factor self-learning value is obtained through weighted calculation. Before weighting the historical gas quality factor self-learning values ​​of all online vehicles at the gas station, it is necessary to clean up the abnormal values, and then compare the historical gas quality factor self-learning value with the first gas quality factor self-learning value to obtain the absolute value of the difference between the two.

[0111] The first initial value determination module is used to determine the historical gas quality factor self-learning value as the gas quality factor self-learning initial value of the gas quality factor self-learning process in the current driving cycle when the absolute value of the difference is greater than the preset difference;

[0112] Specifically, the normal range of the historical gas quality factor self-learning value and the first gas quality factor self-learning value is between 0.8 and 1.3, and the preset difference is a calibration value, which can be freely calibrated according to actual usage. The conventional preset difference can be 0.4. When the absolute value of the difference is greater than the preset difference, it indicates that the difference between the historical gas quality factor self-learning value and the first gas quality factor self-learning value is too large, indicating that the quality of the fuel added by the gas station is different from the fuel last added by the vehicle. In order to prevent the vehicle from running abnormally with the newly added fuel, the initial value of the quality factor self-learning value is set to the historical gas quality factor self-learning value before the new gas quality factor self-learning is performed.

[0113] The second initial value determination module is used to determine the weighted value of the historical gas quality factor self-learning value and the first gas quality factor self-learning value as the gas quality factor self-learning initial value of the gas quality factor self-learning process in the current driving cycle when the absolute value of the difference is less than or equal to the preset difference;

[0114] Specifically, when the absolute value of the difference is less than or equal to the preset difference, it indicates that the difference between the historical gas quality factor self-learning value and the first gas quality factor self-learning value is not much, and before the vehicle performs a new gas quality factor self-learning, the initial value of the quality factor self-learning value is set to the weighted value of the historical gas quality factor self-learning value and the first gas quality factor self-learning value. The weights of the historical gas quality factor self-learning value and the historical gas quality factor self-learning value in the weighted calculation can be freely calibrated.

[0115] The acquisition module is used to perform a gas quality factor self-learning process of the vehicle in this driving cycle according to the gas quality factor self-learning initial value, and obtain a second gas quality factor self-learning value.

[0116] Specifically, when the vehicle engine operating condition meets the activation condition of the gas quality factor self-learning function, the second gas quality factor self-learning value after the current refueling is learned again based on the gas quality factor self-learning initial value, and the gas quality factor self-learning process of the vehicle in the current driving cycle is controlled by acquiring the historical gas quality factor self-learning value and the first gas quality factor self-learning value in real time to obtain the second gas quality factor self-learning value, thereby realizing real-time correction and optimization of engine parameters, improving the driving safety and stability of the vehicle, and reducing the incidence of traffic accidents.

[0117] It can be seen that before the engine controller has completed the self-learning value of the quality factor of the newly added fuel, based on the historical big data summary and analysis of other similar vehicles after refueling at the gas station, combined with the gas quality factor learned by the engine controller before refueling, a reasonable initial value of the self-learning value of the gas quality factor after refueling is given, which prevents the abnormal engine performance index caused by the large difference in fuel quality between the two refuelings. That is, by comparing the absolute value of the historical gas quality factor self-learning value and the first gas quality factor self-learning value, the gas quality factor self-learning process of the vehicle in this driving cycle is adjusted to obtain the second gas quality factor self-learning value, so that the change of the gas quality factor can be more accurately predicted, the engine parameters can be corrected and optimized in real time, and the engine performance can be improved.

[0118] As a possible implementation manner, the control unit further includes: a first determining module, a first correcting module and a second correcting module.

[0119] The first determination module is used to determine whether the second gas quality factor self-learning value is within a first preset range;

[0120] Specifically, after the gas quality factor self-learning value after this refueling is learned based on the gas quality factor self-learning initial value to obtain the second gas quality factor self-learning value, it is necessary to verify the second gas quality factor self-learning value to determine whether the second gas quality factor self-learning value is within a first preset range. In an embodiment of the present application, the upper limit of the first preset range is set to 1.2 and the lower limit is set to 0.8.

[0121] The first correction module is used to correct the EGR rate, ignition advance angle, gas injection amount and air amount of the engine using the second gas quality factor self-learning value when the second gas quality factor self-learning value is within a first preset range;

[0122] Specifically, when the second gas quality factor self-learning value is within the first preset range, it means that the gas quality of this filling is within the reasonable range of conventional settings, and the conventional scheme can be used to correct the relevant parameters of the engine. The conventional scheme is: using the second gas quality factor self-learning value to correct the engine's EGR rate, ignition advance angle, gas injection amount and air amount.

[0123] The second correction module is used to adjust the gas injection amount and the air amount using the second gas quality factor self-learning value when the second gas quality factor self-learning value is not within the first preset range, and update the EGR rate MAP and the ignition advance angle MAP based on the second gas quality factor self-learning value.

[0124] Specifically, when the second gas quality factor self-learning value is not within the first preset range, it means that the quality of the gas filled this time exceeds the conventional setting range. Correcting the relevant parameters of the engine through conventional solutions can no longer well guarantee the relevant performance indicators of the engine. It is necessary to modify and upgrade the data MAP related to the gas quality factor self-learning value and affecting the engine performance indicators according to the second gas quality factor self-learning value. The specific method can be to automatically obtain and send down the data related to the engine performance indicators for upgrading after checking the historical experience data stored in the new cloud platform through the second gas quality factor self-learning value, or it can be that after the Tbox sends back the second gas quality factor self-learning value, the relevant personnel reset the above MAP data and then send down the data related to the engine performance indicators for remote automatic upgrade.

[0125] It can be seen from this that after the engine controller completes the self-learning value of the gas quality factor of the newly added fuel, the rationality of the newly learned quality factor self-learning of the vehicle's engine controller is checked in combination with the historical fuel quality factor self-learning values ​​of other similar vehicles after refueling at the gas station. When it exceeds the preset range, the gas injection amount and air amount are corrected while modifying the engine performance data such as the EGR rate demand basic MAP and the ignition advance angle basic MAP, and the data is upgraded through OTA to achieve optimal control of the engine performance indicators.

[0126] As a possible implementation, the control unit further includes: a ratio determination module, an ignition control module, a gas control module and an air control module.

[0127] The ratio determination module is used to determine the ratio of the exhaust gas volume entering the cylinder of the engine to the total intake air sucked into the cylinder according to the corrected EGR rate; the ignition control module is used to control the engine ignition according to the corrected ignition advance angle; the gas control module is used to control the injection valve of the engine to inject gas with a volume equal to the corrected gas injection volume; the air control module is used to control the intake volume of the cylinder of the engine to be equal to the corrected air volume.

[0128] It can be seen that by adjusting the engine's EGR rate, ignition advance angle, gas injection amount, air volume and other parameters in real time according to the second gas quality factor self-learning value, it is possible to more accurately adapt to changes in gas quality, improve engine combustion efficiency, reduce fuel consumption, and thus reduce the owner's fuel expenses. By making multiple judgments based on the second gas quality factor self-learning value to decide how to adjust parameters such as gas injection amount and air volume, the system's intelligence is enhanced, and the system's adaptability and stability are improved.

[0129] As a possible implementation, the device further includes a history acquisition unit, which is used to acquire historical gas quality factor self-learning values; wherein the history acquisition unit includes: a history acquisition module, a removal module and a history determination module. The history acquisition module is used to acquire historical self-learning values ​​of each online vehicle that refuels at the gas station; the removal module is used to remove abnormal values ​​from multiple historical self-learning values ​​to obtain multiple non-abnormal historical self-learning values; the history determination module is used to determine the weighted value of multiple non-abnormal historical self-learning values ​​as the historical gas quality factor self-learning value.

[0130] It can be seen that by obtaining the historical self-learning values ​​of each online vehicle refueling at the gas station and removing abnormal values, multiple non-abnormal historical self-learning values ​​are obtained, and then the historical gas quality factor self-learning values ​​are calculated, the validity and reliability of the historical data can be improved.

[0131] As a possible implementation method, the device also includes a second determination unit and an abnormal determination unit. The second determination unit is used to determine whether each historical self-learning value is within a second preset range; the abnormal determination unit is used to determine the historical self-learning value that is not within the second preset range as an abnormal value.

[0132] Specifically, by determining whether each historical self-learning value is within the second preset range, historical self-learning values ​​that meet the requirements can be accurately screened out, thereby improving the validity and reliability of historical data.

[0133] It can be seen that by calculating the historical gas quality factor self-learning value based on the screened historical self-learning value, and adjusting the engine's EGR rate, ignition advance angle, gas injection amount, air volume and other parameters, it is possible to more accurately adapt to changes in gas quality and improve the engine's adaptability and stability.

[0134] As a possible implementation manner, the first determination unit includes: an identification acquisition module and a setting determination module.

[0135] The identification acquisition module is used to acquire the setting identification information, when the setting identification information is a first value, it indicates that the fuel filling state is set, and when the setting identification information is a second value, it indicates that the fuel filling state is not set;

[0136] Specifically, by acquiring the setting identification information and its corresponding value, it is possible to accurately determine whether the fuel filling status is set, thereby avoiding misjudgment or missed judgment, and improving the accuracy and reliability of the system.

[0137] The setting determination module is used to determine whether the fuel filling state is set according to the setting identification information.

[0138] Specifically, by determining whether the fuel filling state is set, it is possible to accurately determine which data in the historical data set is related to filling, thereby improving the reliability and effectiveness of the historical data. Therefore, according to the setting of the fuel filling state, the engine's EGR rate, ignition advance angle, gas injection amount, air volume and other parameters can be adjusted, which can more accurately adapt to fuel changes and improve the adaptability and stability of the engine.

[0139] As a possible implementation, the second determination unit includes: a download module, a positioning module and a positioning confirmation module. The download module is used to download the navigation map from the cloud; the positioning module is used to locate the vehicle in the navigation map according to the vehicle's location information to obtain a positioning result; the positioning confirmation module is used to determine the gas station closest to the vehicle in the navigation map according to the positioning result.

[0140] Specifically, by locating the vehicle in the navigation map according to the vehicle's location information and determining the gas station closest to the vehicle, the gas station closest to the vehicle can be accurately determined, avoiding the vehicle missing the nearest gas station due to inaccurate location information, thereby improving the user experience.

[0141] It can be seen that by downloading the navigation map, locating the vehicle and determining the nearest gas station, the nearest gas station can be accurately determined, the gas filling efficiency can be improved, the operating costs can be reduced, the safety and reliability of the vehicle can be enhanced, and the intelligence of the system can be enhanced.

[0142] The parameter correction device of the natural gas engine includes a processor and a memory. The acquisition unit, the first determination unit, the second determination unit and the control unit are all stored in the memory as program units, and the processor executes the program units stored in the memory to realize the corresponding functions. The modules are all located in the same processor; or, the modules are located in different processors in any combination.

[0143] The processor includes a kernel, and the kernel retrieves the corresponding program unit from the memory. The kernel can be set to one or more, and the technical problem of low accuracy of the gas quality factor self-learning solution in the related technology in correcting the engine related data can be solved by adjusting the kernel parameters.

[0144] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0145] An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, a device where the computer-readable storage medium is located is controlled to execute a method for correcting parameters of a natural gas engine.

[0146] Step S201, obtaining a first gas quality factor self-learning value obtained by the vehicle in the previous driving cycle, and a fuel filling state of the vehicle in the current driving cycle, wherein one driving cycle is a driving process from ECU power-on to ECU power-off, and the fuel filling state is used to indicate whether fuel is being added to the vehicle;

[0147] Step S202, determining whether the fuel filling state is set, and if the fuel filling state is set, obtaining the position information of the vehicle;

[0148] Step S203, determining the gas station closest to the vehicle and the historical gas quality factor self-learning values ​​of all online vehicles that refuel at the gas station according to the location information of the vehicle;

[0149] Step S204, controlling the vehicle's gas quality factor self-learning process in the current driving cycle according to the historical gas quality factor self-learning value and the first gas quality factor self-learning value to obtain a second gas quality factor self-learning value in the current driving cycle, and correcting at least the engine performance parameter according to the second gas quality factor self-learning value.

[0150] Optionally, a gas quality factor self-learning process of the vehicle in this driving cycle is controlled according to the historical gas quality factor self-learning value and the first gas quality factor self-learning value to obtain a second gas quality factor self-learning value in this driving cycle, including: obtaining the absolute value of the difference between the historical gas quality factor self-learning value and the first gas quality factor self-learning value; when the absolute value of the difference is greater than a preset difference, determining the historical gas quality factor self-learning value as the gas quality factor self-learning initial value of the gas quality factor self-learning process in the current driving cycle; when the absolute value of the difference is less than or equal to the preset difference, determining a weighted value of the historical gas quality factor self-learning value and the first gas quality factor self-learning value as the gas quality factor self-learning initial value of the gas quality factor self-learning process in the current driving cycle; and performing the gas quality factor self-learning process of the vehicle in this driving cycle according to the gas quality factor self-learning initial value to obtain the second gas quality factor self-learning value.

[0151] Optionally, correcting the engine performance parameters according to the second gas quality factor self-learning value includes: determining whether the second gas quality factor self-learning value is within a first preset range; when the second gas quality factor self-learning value is within the first preset range, using the second gas quality factor self-learning value to correct the engine's EGR rate, ignition advance angle, gas injection amount and air amount; when the second gas quality factor self-learning value is not within the first preset range, using the second gas quality factor self-learning value to adjust the gas injection amount and the air amount, and updating the EGR rate MAP and the ignition advance angle MAP based on the second gas quality factor self-learning value.

[0152] Optionally, when the second gas quality factor self-learning value is within the first preset range, after using the second gas quality factor self-learning value to correct the engine's EGR rate, ignition advance angle, gas injection amount and air amount, the method also includes: determining the ratio of the exhaust gas amount entering the engine's cylinder to the total intake air sucked into the cylinder according to the corrected EGR rate; controlling the engine ignition according to the corrected ignition advance angle; controlling the engine's injection valve to inject gas with a volume equal to the corrected gas injection amount; and controlling the engine's cylinder to inhale air with a volume equal to the corrected air amount.

[0153] Optionally, the method also includes: obtaining historical gas quality factor self-learning values; wherein the step of obtaining historical gas quality factor self-learning values ​​includes: obtaining historical self-learning values ​​of each online vehicle that refuels at a gas station; removing abnormal values ​​from multiple historical self-learning values ​​to obtain multiple non-abnormal historical self-learning values; and determining the weighted value of the multiple non-abnormal historical self-learning values ​​as the historical gas quality factor self-learning value.

[0154] Optionally, before removing abnormal values ​​from multiple historical self-learning values ​​to obtain multiple non-abnormal historical self-learning values, the method also includes: determining whether each historical self-learning value is within a second preset range; and determining historical self-learning values ​​that are not within the second preset range as abnormal values.

[0155] Optionally, determining whether the fuel filling status is set includes: obtaining setting identification information, indicating that the fuel filling status is set when the setting identification information is a first value, and indicating that the fuel filling status is not set when the setting identification information is a second value; determining whether the fuel filling status is set according to the setting identification information.

[0156] Optionally, determining a gas station closest to the vehicle based on the vehicle's location information includes: downloading a navigation map from the cloud; locating the vehicle in the navigation map based on the vehicle's location information to obtain a positioning result; and determining a gas station closest to the vehicle in the navigation map based on the positioning result.

[0157] An embodiment of the present invention provides a processor, which is used to run a program, wherein the program executes a parameter correction method of a natural gas engine when it is run.

[0158] Specifically, the parameter correction method of the natural gas engine includes:

[0159] Step S201, obtaining a first gas quality factor self-learning value obtained by the vehicle in the previous driving cycle, and a fuel filling state of the vehicle in the current driving cycle, wherein one driving cycle is a driving process from ECU power-on to ECU power-off, and the fuel filling state is used to indicate whether fuel is being added to the vehicle;

[0160] Step S202, determining whether the fuel filling state is set, and if the fuel filling state is set, obtaining the position information of the vehicle;

[0161] Step S203, determining the gas station closest to the vehicle and the historical gas quality factor self-learning values ​​of all online vehicles that refuel at the gas station according to the location information of the vehicle;

[0162] Step S204, controlling the vehicle's gas quality factor self-learning process in the current driving cycle according to the historical gas quality factor self-learning value and the first gas quality factor self-learning value to obtain a second gas quality factor self-learning value in the current driving cycle, and correcting at least the engine performance parameter according to the second gas quality factor self-learning value.

[0163] Optionally, a gas quality factor self-learning process of the vehicle in this driving cycle is controlled according to the historical gas quality factor self-learning value and the first gas quality factor self-learning value to obtain a second gas quality factor self-learning value in this driving cycle, including: obtaining the absolute value of the difference between the historical gas quality factor self-learning value and the first gas quality factor self-learning value; when the absolute value of the difference is greater than a preset difference, determining the historical gas quality factor self-learning value as the gas quality factor self-learning initial value of the gas quality factor self-learning process in the current driving cycle; when the absolute value of the difference is less than or equal to the preset difference, determining a weighted value of the historical gas quality factor self-learning value and the first gas quality factor self-learning value as the gas quality factor self-learning initial value of the gas quality factor self-learning process in the current driving cycle; and performing the gas quality factor self-learning process of the vehicle in this driving cycle according to the gas quality factor self-learning initial value to obtain the second gas quality factor self-learning value.

[0164] Optionally, correcting the engine performance parameters according to the second gas quality factor self-learning value includes: determining whether the second gas quality factor self-learning value is within a first preset range; when the second gas quality factor self-learning value is within the first preset range, using the second gas quality factor self-learning value to correct the engine's EGR rate, ignition advance angle, gas injection amount and air amount; when the second gas quality factor self-learning value is not within the first preset range, using the second gas quality factor self-learning value to adjust the gas injection amount and the air amount, and updating the EGR rate MAP and the ignition advance angle MAP based on the second gas quality factor self-learning value.

[0165] Optionally, when the second gas quality factor self-learning value is within the first preset range, after using the second gas quality factor self-learning value to correct the engine's EGR rate, ignition advance angle, gas injection amount and air amount, the method also includes: determining the ratio of the exhaust gas amount entering the engine's cylinder to the total intake air sucked into the cylinder according to the corrected EGR rate; controlling the engine ignition according to the corrected ignition advance angle; controlling the engine's injection valve to inject gas with a volume equal to the corrected gas injection amount; and controlling the engine's cylinder to inhale air with a volume equal to the corrected air amount.

[0166] Optionally, the method also includes: obtaining historical gas quality factor self-learning values; wherein the step of obtaining historical gas quality factor self-learning values ​​includes: obtaining historical self-learning values ​​of each online vehicle that refuels at a gas station; removing abnormal values ​​from multiple historical self-learning values ​​to obtain multiple non-abnormal historical self-learning values; and determining the weighted value of the multiple non-abnormal historical self-learning values ​​as the historical gas quality factor self-learning value.

[0167] Optionally, before removing abnormal values ​​from multiple historical self-learning values ​​to obtain multiple non-abnormal historical self-learning values, the method also includes: determining whether each historical self-learning value is within a second preset range; and determining historical self-learning values ​​that are not within the second preset range as abnormal values.

[0168] Optionally, determining whether the fuel filling status is set includes: obtaining setting identification information, indicating that the fuel filling status is set when the setting identification information is a first value, and indicating that the fuel filling status is not set when the setting identification information is a second value; determining whether the fuel filling status is set according to the setting identification information.

[0169] Optionally, determining a gas station closest to the vehicle based on the vehicle's location information includes: downloading a navigation map from the cloud; locating the vehicle in the navigation map based on the vehicle's location information to obtain a positioning result; and determining a gas station closest to the vehicle in the navigation map based on the positioning result.

[0170] An embodiment of the present invention provides a device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. The device in this article may be a server, a PC, a PAD, a mobile phone, etc. When the processor executes the program, at least the steps of the parameter correction method of the natural gas engine are implemented.

[0171] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that initializes at least the steps of the parameter correction method of the natural gas engine described above.

[0172] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0173] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0174] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0175] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0176] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0177] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0178] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0179] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0180] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity 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, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0181] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0182] 1) An embodiment of the present application provides a parameter correction method for a natural gas engine, firstly obtaining a first gas quality factor self-learning value obtained by the vehicle in the previous driving cycle and a fuel filling status of the vehicle in this driving cycle, wherein one driving cycle is a driving process from ECU power-on to ECU power-off, and the fuel filling status is used to indicate whether fuel is being added to the vehicle; then determining whether the fuel filling status is set, and obtaining the location information of the vehicle when the fuel filling status is set; then determining the gas station closest to the vehicle and the historical gas quality factor self-learning values ​​of all online vehicles that refueled at the gas station according to the location information of the vehicle; finally, controlling the gas quality factor self-learning process of the vehicle in this driving cycle according to the historical gas quality factor self-learning value and the first gas quality factor self-learning value to obtain a second gas quality factor self-learning value in this driving cycle, and correcting at least the engine performance parameter according to the second gas quality factor self-learning value. In the embodiment of the present application, the self-learning process of the current gas quality factor is performed in combination with the self-learning value of the gas quality factor obtained in the previous cycle and the self-learning value of the gas quality factor learned historically, so as to ensure the accuracy of the correction of the engine performance parameters, and solve the technical problem that the gas quality factor self-learning scheme in the related art has a low accuracy rate in correcting the engine related data.

[0183] 2) An embodiment of the present application provides a parameter correction device for a natural gas engine, the device comprising: an acquisition unit, a first determination unit, a second determination unit and a control unit, the acquisition unit being used to acquire a first gas quality factor self-learning value obtained by the vehicle in a previous driving cycle, and a fuel filling state of the vehicle in this driving cycle, wherein a driving cycle is a driving process from ECU power-on to ECU power-off, and the fuel filling state is used to indicate whether fuel is being added to the vehicle; the first determination unit is used to determine whether the fuel filling state is set, and when the fuel filling state is set, acquire the location information of the vehicle; the second determination unit is used to determine the gas station closest to the vehicle and the historical gas quality factor self-learning values ​​of all online vehicles that refuel at the gas station according to the location information of the vehicle; the control unit is used to control the gas quality factor self-learning process of the vehicle in this driving cycle according to the historical gas quality factor self-learning value and the first gas quality factor self-learning value, so as to obtain the second gas quality factor self-learning value in this driving cycle, and at least correct the engine performance parameter according to the second gas quality factor self-learning value. The device combines the gas quality factor self-learning value obtained in the previous cycle with the gas quality factor self-learning value learned historically to perform the current gas quality factor self-learning process, thereby ensuring the accuracy of engine performance parameter correction and solving the technical problem of low accuracy of gas quality factor self-learning scheme in related technologies in correcting engine-related data.

[0184] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for correcting parameters of a natural gas engine, characterized in that: The natural gas engine is installed on a vehicle and comprises: Obtaining a first gas quality factor self-learning value obtained by the vehicle in a previous driving cycle, and a fuel filling state of the vehicle in this driving cycle, wherein one driving cycle is a driving process from ECU power-on to ECU power-off, and the fuel filling state is used to indicate whether fuel is being added to the vehicle; determining whether the fuel filling state is set, and obtaining the position information of the vehicle if the fuel filling state is set; Determine the gas station closest to the vehicle and the historical gas quality factor self-learning values ​​of all online vehicles that refuel at the gas station according to the location information of the vehicle; A gas quality factor self-learning process of the vehicle in the current driving cycle is controlled according to the historical gas quality factor self-learning value and the first gas quality factor self-learning value to obtain a second gas quality factor self-learning value in the current driving cycle, and at least an engine performance parameter is corrected according to the second gas quality factor self-learning value.

2. The method according to claim 1, characterized in that Controlling the self-learning process of the gas quality factor of the vehicle in the current driving cycle according to the historical gas quality factor self-learning value and the first gas quality factor self-learning value to obtain a second gas quality factor self-learning value in the current driving cycle includes: Obtaining an absolute value of a difference between the historical gas quality factor self-learning value and the first gas quality factor self-learning value; When the absolute value of the difference is greater than the preset difference, the historical gas quality factor self-learning value is determined as the gas quality factor self-learning initial value of the gas quality factor self-learning process in the current driving cycle; When the absolute value of the difference is less than or equal to the preset difference, a weighted value of the historical gas quality factor self-learning value and the first gas quality factor self-learning value is determined as a gas quality factor self-learning initial value of the gas quality factor self-learning process in the current driving cycle; A gas quality factor self-learning process of the vehicle in the current driving cycle is performed according to the gas quality factor self-learning initial value to obtain the second gas quality factor self-learning value.

3. The method according to claim 1, characterized in that Correcting engine performance parameters according to the second fuel gas quality factor self-learning value includes: Determining whether the second gas quality factor self-learning value is within a first preset range; When the second gas quality factor self-learning value is within the first preset range, the second gas quality factor self-learning value is used to correct the EGR rate, ignition advance angle, gas injection amount and air amount of the engine; When the second gas quality factor self-learning value is not within the first preset range, the gas injection amount and the air amount are adjusted using the second gas quality factor self-learning value, and the EGR rate MAP and the ignition advance angle MAP are updated based on the second gas quality factor self-learning value.

4. The method according to claim 3, characterized in that When the second gas quality factor self-learning value is within the first preset range, after using the second gas quality factor self-learning value to correct the EGR rate, ignition advance angle, gas injection amount and air amount of the engine, the method further includes: determining a ratio of an amount of exhaust gas entering a cylinder of the engine to a total amount of intake air sucked into the cylinder according to the corrected EGR rate; Controlling the engine ignition according to the corrected ignition advance angle; The injection valve of the engine is controlled to inject gas with a volume amount equal to the corrected gas injection amount; and the cylinder of the engine is controlled to inhale air with a volume amount equal to the corrected air amount.

5. The method according to claim 1, characterized in that The method further comprises: Obtaining the historical gas quality factor self-learning value; The step of obtaining the historical gas quality factor self-learning value includes: Obtaining historical self-learning values ​​of each of the online vehicles that refuel at the gas station; Removing abnormal values ​​from the plurality of historical self-learning values ​​to obtain a plurality of non-abnormal historical self-learning values; A weighted value of the plurality of non-abnormal historical self-learning values ​​is determined as the historical gas quality factor self-learning value.

6. The method according to claim 5, characterized in that Before removing abnormal values ​​from the plurality of historical self-learning values ​​to obtain a plurality of non-abnormal historical self-learning values, the method further includes: Determining whether each of the historical self-learning values ​​is within a second preset range; The historical self-learning value that is not within the second preset range is determined as the abnormal value.

7. The method according to any one of claims 1 to 6, characterized in that Determining whether the fuel filling state is set includes: Acquire setting identification information, wherein when the setting identification information is a first value, it indicates that the fuel filling state is set, and when the setting identification information is a second value, it indicates that the fuel filling state is not set; Determine whether the fuel filling state is set according to the setting identification information.

8. The method according to any one of claims 1 to 6, characterized in that Determining the gas station closest to the vehicle according to the location information of the vehicle includes: Download navigation maps from the cloud; The vehicle is located in the navigation map according to the location information of the vehicle to obtain a positioning result; and the gas station closest to the vehicle is determined in the navigation map according to the positioning result.

9. A parameter correction device for a natural gas engine, characterized in that: The natural gas engine is installed on a vehicle and comprises: an acquisition unit, used for acquiring a first gas quality factor self-learning value obtained by the vehicle in a previous driving cycle, and a fuel filling state of the vehicle in this driving cycle, wherein one driving cycle is a driving process from ECU power-on to ECU power-off, and the fuel filling state is used for indicating whether fuel is being added to the vehicle; a first determining unit, configured to determine whether the fuel filling state is set, and to obtain the position information of the vehicle when the fuel filling state is set; A second determination unit is used to determine the gas station closest to the vehicle and the historical gas quality factor self-learning values ​​of all online vehicles that refuel at the gas station according to the location information of the vehicle; a control unit, configured to control a gas quality factor self-learning process of the vehicle in the current driving cycle according to the historical gas quality factor self-learning value and the first gas quality factor self-learning value, so as to obtain a second gas quality factor self-learning value in the current driving cycle, and to correct at least an engine performance parameter according to the second gas quality factor self-learning value.

10. An electronic device, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing the parameter correction method of the natural gas engine described in any one of claims 1 to 8.

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