Calibration method and device for engine torque, vehicle controller

Abstract

The application provides an engine torque calibration method and device and a vehicle controller, and belongs to the field of vehicle control, wherein the method comprises the following steps: obtaining an engine target torque and a generator signal torque of a hybrid vehicle; calculating a torque deviation of the hybrid vehicle according to the engine target torque and the generator signal torque; calculating an engine demand torque of the hybrid vehicle based on self-learning of the torque deviation, and calibrating the engine signal torque based on the engine demand torque, so that the engine signal torque, the generator signal torque and the engine target torque of the hybrid vehicle are consistent. Through the embodiment of the application, the problems that the real engine torque is too large to cause poor fuel consumption performance and poor drivability in low-temperature extreme working conditions are solved, the problem that the real engine torque is too small to cause unbalanced electric quantity in the emission cycle test process is solved, the fuel consumption performance of the vehicle is improved by improving the torque accuracy, the low-temperature driving performance of the vehicle is improved, and the electric quantity is more easily balanced.

Description

Technical Field

[0001] This invention relates to the field of vehicle control, and more specifically, to a method and apparatus for calibrating engine torque and a vehicle controller. Background Technology

[0002] In related technologies, engine torque accuracy refers to the difference between the torque signal (signal torque) sent by the engine to the Controller Area Network (CAN) and the actual engine torque (actual torque), expressed as a percentage. The calculation formula is: (signal torque - actual torque) / signal torque × 100%. The engine's signal torque is calculated using a torque model. Due to manufacturing variations in the engine and intake / exhaust systems, wheel-side force tests conducted at the wheel well reveal that the engine torque accuracy in vehicle applications is typically within ±10%.

[0003] With the development of hybrid vehicle technology, the requirements for engine torque accuracy from the vehicle controller are becoming increasingly stringent, requiring an accuracy within ±3%. In P13 and REEV (range-extended electric vehicle) hybrid models, the engine and generator are mechanically connected, and the generator's torque is relatively accurate, within ±3%. Current calibration methods cannot resolve torque deviations caused by manufacturing variations or environmental changes, and require manual calibration, which is not intelligent enough.

[0004] No efficient and accurate solution has yet been found to address the aforementioned issues in the relevant technologies. Summary of the Invention

[0005] This invention provides a method and apparatus for calibrating engine torque, as well as a vehicle controller, to solve technical problems in related technologies.

[0006] According to an embodiment of the present invention, an engine torque calibration method is provided, comprising: acquiring a target engine torque and a generator signal torque of a hybrid vehicle; calculating a torque deviation of the hybrid vehicle based on the target engine torque and the generator signal torque; calculating a required engine torque of the hybrid vehicle based on the torque deviation through self-learning; and calibrating the engine signal torque based on the required engine torque to make the engine signal torque, the generator signal torque, and the target engine torque of the hybrid vehicle consistent.

[0007] Furthermore, calculating the torque deviation of the hybrid vehicle based on the engine target torque and the generator signal torque includes: obtaining the engine target torque deviation by subtracting the historical engine signal torque of the previous control cycle from the engine target torque; obtaining the engine actual torque deviation by subtracting the generator signal torque from the historical engine signal torque of the previous control cycle; and obtaining the calculated engine control torque deviation by subtracting the generator signal torque from the engine target torque; wherein the torque deviation includes: the engine target torque deviation, the engine actual torque deviation, and the engine control torque deviation.

[0008] Furthermore, the self-learning calculation of the engine torque requirement of the hybrid vehicle based on the torque deviation includes: locating a self-learning region based on the current engine parameters of the hybrid vehicle; determining whether it is permissible to write a self-learning torque value in the self-learning region based on the engine target torque deviation and the engine actual torque deviation; if it is permissible to write a self-learning torque value in the self-learning region, performing self-learning torque calculation in the self-learning region based on the engine control torque deviation, and storing the self-learning torque value in the corresponding storage unit; reading the self-learning torque value from the storage unit, and adding the self-learning torque value to the engine target torque to obtain the engine torque requirement.

[0009] Furthermore, locating the self-learning region based on the current engine parameters of the hybrid vehicle includes: setting parameter thresholds for multiple engine operating parameters, wherein the parameter thresholds include X engine speed thresholds, Y engine load thresholds, and Z engine temperature thresholds, where X, Y, and Z are all positive integers greater than 0; dividing the number of engine storage units using the number of parameter thresholds, wherein each storage unit is used to store a self-learning value; obtaining the current engine parameters of the hybrid vehicle; obtaining a parameter index by comparing the current engine parameters with the corresponding parameter thresholds, wherein the parameter index includes an engine speed index, an engine load index, and an engine temperature index, and the parameter index is used to characterize the threshold range in which the current engine parameters are located; and locating the storage unit number of the self-learning region using the parameter index.

[0010] Furthermore, the method of dividing the number of engine storage units using the parameter threshold includes: dividing the engine operating area into (X+1)×(Y+1) regions; and dividing the number of engine storage units into (X+1)×(Y+1)×Z units.

[0011] Furthermore, the storage unit number of the self-learning region is located using the parameter index, which includes: calculating the storage unit number i using the following formula: i = engine speed index × Z + engine load index × (X+1) × Z + engine temperature index.

[0012] Furthermore, determining whether to allow writing self-learning torque values ​​in the self-learning area based on the engine target torque deviation and the engine actual torque deviation includes: determining the stability flags of the hybrid vehicle, wherein the stability flags include: an engine target torque stability flag, an engine speed stability flag, and a generator torque stability flag; determining whether all the stability flags are in a set state, and determining whether the real-time operating conditions of the hybrid vehicle meet all of the following preset conditions: engine coolant temperature is greater than a set value; engine start-up is completed and lasts for a certain period of time; engine is in torque control mode; the absolute value of the engine target torque deviation is less than or equal to a first threshold; the absolute value of the engine actual torque deviation is greater than or equal to a second threshold; the engine target torque is greater than or equal to a third threshold; if all the stability flags are in a set state, and the real-time operating conditions of the hybrid vehicle meet all preset conditions, it is determined that self-learning writing is allowed.

[0013] Furthermore, performing self-learning torque calculation based on the engine control torque deviation within the self-learning region and storing the self-learning value in the corresponding storage unit includes: determining whether the engine control torque deviation is greater than or equal to a deviation threshold, and comparing the self-learning torque value of the self-learning region with the threshold value; if the engine control torque deviation is greater than or equal to a first deviation threshold and the self-learning torque value is less than an upper limit value, it is determined to be a first self-learning state; if the engine control torque deviation is less than or equal to a second deviation threshold and the self-learning torque value is greater than a lower limit value, it is determined to be a second self-learning state; if the engine control torque deviation is less than the first deviation threshold, or the engine control torque deviation is greater than the second deviation threshold, or the self-learning torque value is equal to the upper or lower limit value, it is determined to be a third self-learning state; if it is a first self-learning state, the self-learning torque value is increased at a preset rate; if it is a second self-learning state, the self-learning torque value is decreased at a preset rate; if it is a third self-learning state, the current self-learning torque value of the self-learning region is maintained; and the corresponding self-learning torque value is updated in real time in the storage unit of the self-learning region.

[0014] Furthermore, calibrating the engine signal torque based on the engine demand torque includes: running the engine of the hybrid vehicle according to the engine demand torque, detecting the actual torque of the engine, determining the actual torque as the engine calculated torque, and subtracting the self-learned torque value from the engine calculated torque to obtain the engine signal torque.

[0015] According to another embodiment of the present invention, an engine torque calibration device is provided, comprising: an acquisition module for acquiring a target engine torque and a generator signal torque of a hybrid vehicle; a calculation module for calculating a torque deviation of the hybrid vehicle based on the target engine torque and the generator signal torque; and a calibration module for self-learning to calculate the required engine torque of the hybrid vehicle based on the torque deviation, and calibrating the engine signal torque based on the required engine torque, so that the engine signal torque, the generator signal torque, and the target engine torque of the hybrid vehicle are consistent.

[0016] Furthermore, the calculation module includes: a first calculation unit, used to obtain the engine target torque deviation by subtracting the historical engine signal torque of the previous control cycle from the engine target torque; a second calculation unit, used to obtain the engine actual torque deviation by subtracting the generator signal torque from the historical engine signal torque of the previous control cycle; and a third calculation unit, used to obtain the calculated engine control torque deviation by subtracting the generator signal torque from the engine target torque; wherein the torque deviation includes: the engine target torque deviation, the engine actual torque deviation, and the engine control torque deviation.

[0017] Furthermore, the calibration module includes: a positioning unit, used to locate the self-learning region based on the current engine parameters of the hybrid vehicle; a judgment unit, used to determine whether it is permissible to write a self-learning torque value in the self-learning region based on the engine target torque deviation and the engine actual torque deviation; an execution unit, used to perform self-learning torque calculation in the self-learning region based on the engine control torque deviation if it is permissible to write the self-learning torque value in the self-learning region, and store the self-learning torque value in the corresponding storage unit; and a first calibration unit, used to read the self-learning torque value from the storage unit, and obtain the engine required torque by adding the engine target torque to the self-learning torque value.

[0018] Furthermore, the positioning unit includes: a setting subunit for setting parameter thresholds for multiple engine operating parameters, wherein the parameter thresholds include X engine speed thresholds, Y engine load thresholds, and Z engine temperature thresholds, where X, Y, and Z are all positive integers greater than 0; a partitioning subunit for partitioning the number of engine storage units using the number of parameter thresholds, wherein each storage unit is used to store a self-learning value; an acquisition subunit for acquiring the current engine parameters of the hybrid vehicle; a comparison subunit for obtaining a parameter index by comparing the current engine parameters with the corresponding parameter thresholds, wherein the parameter index includes an engine speed index, an engine load index, and an engine temperature index, and the parameter index is used to characterize the threshold range in which the current engine parameters are located; and a positioning subunit for locating the storage unit number of the self-learning region using the parameter index.

[0019] Furthermore, the partitioning subunit is also used to: divide the engine operating area into (X+1)×(Y+1) areas; and divide the number of engine storage units into (X+1)×(Y+1)×Z units.

[0020] Furthermore, the positioning subunit is also used to calculate the storage unit number i using the following formula: i = engine speed index × Z + engine load index × (X+1) × Z + engine temperature index.

[0021] Furthermore, the determination unit includes: a first determining subunit, used to determine the stability indicators of the hybrid vehicle, wherein the stability indicators include: an engine target torque stability indicator, an engine speed stability indicator, and a generator torque stability indicator; a determining subunit, used to determine whether all the stability indicators are in a set state, and to determine whether the real-time operating condition of the hybrid vehicle meets all of the following preset conditions: engine coolant temperature is greater than a set value; engine start-up is completed and lasts for a certain period of time; engine is in torque control mode; the absolute value of the engine target torque deviation is less than or equal to a first threshold; the absolute value of the engine actual torque deviation is greater than or equal to a second threshold; the engine target torque is greater than or equal to a third threshold; a second determining subunit, used to determine that self-learning writing is allowed if all the stability indicators are in a set state and the real-time operating condition of the hybrid vehicle meets all preset conditions.

[0022] Furthermore, the execution unit includes: a processing subunit, used to determine whether the engine control torque deviation is greater than or equal to a deviation threshold, and to compare the self-learning torque value of the self-learning region with a threshold value; a determining subunit, used to determine a first self-learning state if the engine control torque deviation is greater than or equal to a first deviation threshold and the self-learning torque value is less than an upper limit value; determine a second self-learning state if the engine control torque deviation is less than or equal to a second deviation threshold and the self-learning torque value is greater than a lower limit value; determine a third self-learning state if the engine control torque deviation is less than the first deviation threshold, or the engine control torque deviation is greater than the second deviation threshold, or the self-learning torque value is equal to the upper or lower limit value; a learning subunit, used to increase the self-learning torque value at a preset rate if it is a first self-learning state; decrease the self-learning torque value at a preset rate if it is a second self-learning state; and maintain the current self-learning torque value of the self-learning region if it is a third self-learning state; and an updating subunit, used to update the corresponding self-learning torque value in the storage unit of the self-learning region in real time.

[0023] Furthermore, the calibration module includes: a running unit, used to run the engine of the hybrid vehicle according to the engine demand torque, and detect the actual torque of the engine, and determine the actual torque as the engine calculated torque; and a second calibration unit, used to subtract the self-learning torque value from the engine calculated torque to obtain the engine signal torque.

[0024] According to another aspect of the embodiments of this application, a vehicle controller is also provided, including: a generator control unit for transmitting generator signal torque to the engine control unit; an engine control unit for receiving the generator signal torque transmitted by the generator control unit, receiving an engine target torque transmitted by the vehicle control unit, and executing the method described in the above embodiments; and a vehicle control unit for receiving the engine signal torque transmitted by the engine control unit.

[0025] According to another aspect of the embodiments of this application, a storage medium is also provided, the storage medium including a stored program that executes the above steps when the program is run.

[0026] According to another aspect of the embodiments of this application, an electronic device is also provided, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; wherein: the memory is used to store computer programs; and the processor is used to execute the steps in the above method by running the programs stored in the memory.

[0027] This application also provides a computer program product containing instructions that, when run on a computer, cause the computer to perform the steps in the above-described method.

[0028] The beneficial effects of this invention are:

[0029] 1. This invention uses generator signal torque as a verification benchmark to perform engine torque self-learning, automatically adjusts the engine's required torque, and calibrates the engine signal torque, so that the values ​​of the engine target torque, engine signal torque, and generator signal torque are close. This solves the problems of poor fuel consumption performance and poor drivability under low-temperature extreme conditions caused by excessive engine torque, and solves the problem of unbalanced power supply during emission cycle testing caused by insufficient engine torque.

[0030] 1. This invention improves vehicle fuel efficiency and low-temperature driving performance by increasing torque accuracy, and makes it easier to balance the battery power. Attached Figure Description

[0031] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0032] Figure 1 This is a hardware structure block diagram of an engine control module according to an embodiment of the present invention;

[0033] Figure 2 This is a flowchart of an engine torque calibration method according to an embodiment of the present invention;

[0034] Figure 3 This is a calculation logic diagram of the engine target torque deviation in an embodiment of the present invention;

[0035] Figure 4 This is a calculation logic diagram for the actual torque deviation of the engine in this invention;

[0036] Figure 5 This is a calculation logic diagram of the engine control torque deviation in this invention;

[0037] Figure 6 This is a calculation logic diagram of the engine speed index in this invention;

[0038] Figure 7 This is a calculation logic diagram of the engine load index in this invention;

[0039] Figure 8 This is a calculation logic diagram of the engine intake air temperature index in this invention;

[0040] Figure 9 This is a schematic diagram showing the engine operating area corresponding to the storage unit number in this invention;

[0041] Figure 10This is a calculation logic diagram of the engine speed stability indicator in this invention;

[0042] Figure 11 This is a calculation logic diagram of the engine target torque stability indicator in this invention;

[0043] Figure 12 This is a calculation logic diagram of the generator torque stability flag in this invention;

[0044] Figure 13 This is the computational logic diagram of the self-learning state in this invention;

[0045] Figure 14 This is the logic diagram for calculating the self-learning value when the self-learning state is 1 in this invention;

[0046] Figure 15 This is the logic diagram for calculating the self-learning value when the self-learning state is 2 in this invention;

[0047] Figure 16 This is a calculation logic diagram of the self-learning value reading flag in this invention;

[0048] Figure 17 This is a calculation logic diagram of the self-learning read value in this invention;

[0049] Figure 18 This is a flowchart illustrating an embodiment of the present invention;

[0050] Figure 19 This is a structural block diagram of an engine torque calibration device according to an embodiment of the present invention;

[0051] Figure 20 This is a schematic diagram of the vehicle controller in an embodiment of the present invention. Detailed Implementation

[0052] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, and not all of them. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present application. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of the present application can be combined with each other.

[0053] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0054] Example 1

[0055] The method embodiment provided in Embodiment 1 of this application can be executed in an in-vehicle terminal, an in-vehicle control module, an engine control module (engine control unit), a hybrid vehicle, or a similar processing device. Taking its operation on an engine control module as an example, Figure 1 This is a hardware structure block diagram of an engine control module according to an embodiment of the present invention. Figure 1 As shown, the engine control module may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. Optionally, the engine control module may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the engine control module described above. For example, the engine control module may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0056] The memory 104 can be used to store engine control module programs, such as application software programs and modules, like the engine control module program corresponding to an engine torque calibration method in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the engine control module program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the engine control module via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0057] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication supplier of the engine control module. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0058] This embodiment provides a method for calibrating engine torque. Figure 2 This is a flowchart of an engine torque calibration method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0059] Step S202: Obtain the target torque of the engine and the signal torque of the generator of the hybrid vehicle;

[0060] The hybrid vehicle in this embodiment is a vehicle equipped with both an engine and a generator as power units, and can be a plug-in hybrid, mild hybrid, electric hybrid, or other hybrid vehicle types.

[0061] Figure 3 This is a schematic diagram of signal transmission in an embodiment of the present invention. The engine control unit receives the engine target torque sent by the vehicle control unit and the generator signal torque sent by the generator control unit. After generating the calibrated engine signal torque, the engine control unit sends the engine signal torque to the vehicle control unit.

[0062] Step S204: Calculate the torque deviation of the hybrid vehicle based on the engine target torque and the generator signal torque;

[0063] In this embodiment, the torque deviation can be the engine target torque deviation, the engine actual torque deviation, or the engine control torque deviation.

[0064] Step S206: Calculate the engine demand torque of the hybrid vehicle based on torque deviation self-learning, and calibrate the engine signal torque based on the engine demand torque to make the engine signal torque, generator signal torque and engine target torque of the hybrid vehicle consistent.

[0065] In this embodiment, the engine signal torque, generator signal torque, and engine target torque of the hybrid vehicle are consistent when their values ​​are close (the difference is less than the minimum threshold). Through calibration, the values ​​of the three are made to be consistent.

[0066] Through the above steps, the target torque of the hybrid vehicle's engine and the generator signal torque are obtained. The torque deviation of the hybrid vehicle is calculated based on these two values. The required torque of the hybrid vehicle's engine is calculated through self-learning based on the torque deviation, and the engine signal torque is calibrated based on this required torque. This ensures that the engine signal torque, generator signal torque, and target torque of the hybrid vehicle are consistent. This solves the technical problem of low torque accuracy in hybrid vehicles due to manufacturing variations or environmental changes, improving the user experience. By using the generator signal torque as a calibration benchmark for engine torque self-learning, and meeting the self-learning enabling conditions, torque deviation learning can be performed automatically, adjusting the required engine torque and calibrating the engine signal torque. This achieves close values ​​for the target torque, engine signal torque, and generator signal torque, resolving issues such as poor fuel economy due to excessively high actual engine torque and poor drivability under low-temperature extreme conditions. It also solves the problem of unbalanced battery power during emission cycle testing due to excessively low actual engine torque. By improving torque accuracy, vehicle fuel economy performance and low-temperature driving performance are improved, and battery power is more easily balanced.

[0067] In one embodiment of this example, calculating the torque deviation of the hybrid vehicle based on the engine target torque and the generator signal torque includes: obtaining the engine target torque deviation by subtracting the historical engine signal torque of the previous control cycle from the engine target torque; obtaining the engine actual torque deviation by subtracting the generator signal torque of the previous control cycle from the historical engine signal torque; and obtaining the calculated engine control torque deviation by subtracting the generator signal torque from the engine target torque. The torque deviation includes: the engine target torque deviation, the engine actual torque deviation, and the engine control torque deviation.

[0068] This embodiment includes the following three torque deviation calculations when calculating torque deviation: engine target torque deviation, engine actual torque deviation, and engine control torque deviation. These are described in detail below:

[0069] Figure 3 This is the calculation logic diagram of the engine target torque deviation in this embodiment of the invention. The engine target torque after low-pass filtering (filtering time is T1, which can be calibrated) is subtracted from the engine signal torque after low-pass filtering (filtering time is T2, which can be calibrated) to obtain the calculated engine target torque deviation, abbreviated as deviation A.

[0070] Figure 4 This is a calculation logic diagram for the engine's true torque deviation in this invention. The calculation logic for the engine's true torque deviation is as follows: Figure 4 As shown, the engine signal torque (previous torque control cycle) after low-pass filtering (filtering time T3, calibrable) is subtracted from the generator signal torque after low-pass filtering (filtering time T4, calibrable) to obtain the calculated engine true torque deviation, abbreviated as deviation B.

[0071] Figure 5 This is a calculation logic diagram for the engine control torque deviation in this invention. The calculation logic for the engine control torque deviation is as follows: Figure 5 As shown, the engine control torque deviation, abbreviated as deviation C, is obtained by subtracting the generator signal torque after low-pass filtering (filtering time T5, which can be calibrated) from the engine target torque after low-pass filtering (filtering time T6, which can be calibrated).

[0072] In one embodiment of this example, calculating the engine torque requirement of a hybrid vehicle based on torque deviation self-learning includes: locating a self-learning region based on the current engine parameters of the hybrid vehicle; determining whether it is permissible to write a self-learning torque value in the self-learning region based on the engine target torque deviation and the engine actual torque deviation; if it is permissible to write a self-learning torque value in the self-learning region, performing self-learning torque calculation based on the engine control torque deviation in the self-learning region, and storing the self-learning torque value in the corresponding storage unit; reading the self-learning torque value from the storage unit, and obtaining the engine torque requirement by adding the engine target torque to the self-learning torque value.

[0073] In one example based on the above implementation method, locating the self-learning region based on the current engine parameters of the hybrid vehicle includes: setting parameter thresholds for multiple engine operating parameters, wherein the parameter thresholds include X engine speed thresholds, Y engine load thresholds, and Z engine temperature thresholds, where X, Y, and Z are all positive integers greater than 0; dividing the number of engine storage units by the number of parameter thresholds, wherein each storage unit is used to store a self-learning value; obtaining the current engine parameters of the hybrid vehicle; obtaining parameter indexes by comparing the current engine parameters with the corresponding parameter thresholds, wherein the parameter indexes include engine speed indexes, engine load indexes, and engine temperature indexes, and the parameter indexes are used to characterize the threshold range in which the current engine parameters are located; and locating the storage unit number of the self-learning region using the parameter indexes.

[0074] Optionally, the number of engine storage units can be divided using parameter thresholds, including: dividing the engine operating area into (X+1)×(Y+1) regions; and dividing the number of engine storage units into (X+1)×(Y+1)×Z units.

[0075] For example, X, Y, and Z are 4, 3, and 3 respectively. By setting 4 engine speed thresholds, 3 engine load thresholds, and 3 temperature thresholds, the engine operating area is divided into 20 areas, with a total of 60 storage units.

[0076] Optionally, the storage cell number of the self-learning area is located using parameter indexing, including: the storage cell number i is calculated using the following formula: i = engine speed index × Z + engine load index × (X+1) × Z + engine temperature index.

[0077] Figure 6 The calculation logic diagram for the engine speed index in this invention is as follows: four engine speed thresholds N1, N2, N3, and N4 are set, along with a speed threshold hysteresis Nhy. If the engine speed is greater than threshold N4, the engine speed index is equal to 4; if the engine speed is less than or equal to (N4-Nhy) and the engine speed is greater than N3, the engine speed index is equal to 3; if the engine speed is less than or equal to (N3-Nhy) and the engine speed is greater than N2, the engine speed index is equal to 2; if the engine speed is less than or equal to (N2-Nhy) and the engine speed is greater than N1, the engine speed index is equal to 1; and if the engine speed is less than or equal to (N1-Nhy), the engine speed index is equal to 0.

[0078] Figure 7 This is a calculation logic diagram of the engine load index in this invention, as shown below. Figure 7As shown, three engine speed thresholds R1, R2, and R3 are set, along with a load threshold hysteresis Rhy. If the engine load is greater than threshold R3, the engine load index is equal to 3. If the engine load is less than or equal to (R3-Rhy) and the engine load is greater than R2, the engine load index is equal to 2. If the engine load is less than or equal to (R2-Rhy) and the engine load is greater than R1, the engine load index is equal to 1. If the engine load is less than or equal to (R1-Rhy), the engine load index is equal to 0.

[0079] Figure 8 This is a calculation logic diagram for the engine intake air temperature index in this invention, as shown below. Figure 8 As shown, two engine intake air temperature thresholds D1 and D2 are set, as well as a temperature threshold hysteresis Dhy. If the engine intake air temperature is greater than threshold D2, the intake air temperature index is equal to 2. If the intake air temperature is less than or equal to (D2-Dhy) and the intake air temperature is greater than D1, the intake air temperature index is equal to 1. If the intake air temperature is less than or equal to (D1-Dhy), the intake air temperature index is equal to 0.

[0080] The storage unit number i is determined based on the engine speed index, engine load index, and engine temperature index; the formula for calculating the storage unit number is: i = engine speed index × 3 + engine load index × 15 + engine temperature index.

[0081] Under a defined engine operating condition (engine speed, engine load, intake air temperature), the corresponding storage unit can be located by its storage unit number, and each storage unit stores a self-learning value. Figure 9 This is a schematic diagram of the engine operating area corresponding to the storage unit number in this invention. The schematic diagram of the engine operating conditions corresponding to each storage unit number is as follows: Figure 9 As shown.

[0082] In one example based on the above implementation method, determining whether to allow writing self-learning torque values ​​in the self-learning area includes: determining the stability flags of the hybrid vehicle, wherein the stability flags include: an engine target torque stability flag, an engine speed stability flag, and a generator torque stability flag; determining whether all stability flags are in a set state, and determining whether the real-time operating conditions of the hybrid vehicle meet all of the following preset conditions: engine coolant temperature is greater than a set value; engine start-up is completed and lasts for a certain period of time; engine is in torque control mode; the absolute value of the engine target torque deviation is less than or equal to a first threshold; the absolute value of the engine actual torque deviation is greater than or equal to a second threshold; the engine target torque is greater than or equal to a third threshold; if all stability flags are in a set state, and the real-time operating conditions of the hybrid vehicle meet all preset conditions, it is determined that self-learning writing is allowed.

[0083] In this example, the stability indicators for hybrid vehicles include the engine speed stability indicator, the engine target torque stability indicator, and the generator torque stability indicator. These are explained in detail below:

[0084] Figure 10 The calculation logic diagram for the engine speed stability flag in this invention is as follows: the engine speed stability flag is determined by the engine speed change and the corresponding threshold; the engine speed change is obtained by subtracting the engine speed from the engine speed a calculation cycles ago; the engine speed change is first processed by low-pass filtering (filter constant is m1), and then by absolute value processing to obtain the processed engine speed change; if the processed engine speed change is less than or equal to the threshold Nd, the engine speed stability flag is set; if the processed engine speed change is greater than the threshold Nd, the engine speed stability flag is reset.

[0085] Figure 11 This is the calculation logic diagram for the engine target torque stability flag in this invention. The engine target torque stability flag is determined by the engine target torque change and the corresponding threshold. The engine target torque change is obtained by subtracting the engine target torque from the engine target torque ab calculation cycles ago. The engine target torque change is first processed by low-pass filtering (filter constant is m2), and then by absolute value processing to obtain the processed engine target torque change. If the processed engine target torque change is less than or equal to the threshold Tqd1, the engine target torque stability flag is set; if the processed engine target torque change is greater than the threshold Tqd1, the engine target torque stability flag is reset.

[0086] Figure 12 The calculation logic diagram for the generator torque stability flag in this invention is as follows: the generator torque stability flag is determined by the generator torque change and the corresponding threshold; the generator torque change is obtained by subtracting the generator torque from the generator torque before c calculation cycles; the generator torque change is first processed by low-pass filtering (filter constant is m3), and then by absolute value processing to obtain the processed generator torque change; if the processed generator torque change is less than or equal to the threshold Tqd2, the generator torque stability flag is set; if the processed generator torque change is greater than the threshold Tqd2, the generator torque stability flag is reset.

[0087] The self-learning write enable flag is set when all of the following nine conditions are met; the self-learning write enable flag is reset when not all of the following nine conditions are met; all conditions include:

[0088] Condition 1: Engine target torque stability flag is set;

[0089] Condition 2: Engine speed stability indicator is set;

[0090] Condition 3: Generator torque stability flag is set;

[0091] Condition 4: Engine coolant temperature is greater than threshold D3;

[0092] Condition 5: Engine starts and continues for a certain period of time (T7);

[0093] Condition 6: The engine is in torque control mode;

[0094] Condition 7: The absolute value of the torque deviation A is less than or equal to the threshold Tq1;

[0095] Condition 8: The absolute value of the torque deviation B is greater than or equal to the threshold Tq2;

[0096] Condition 9: The target torque of the engine is greater than or equal to the threshold Tq3.

[0097] In one example based on the above implementation, performing self-learning torque calculation within the self-learning region and storing the self-learning value in the corresponding storage unit includes: determining whether the engine control torque deviation is greater than or equal to a deviation threshold, and comparing the self-learning torque value of the self-learning region with the threshold value; if the engine control torque deviation is greater than or equal to a first deviation threshold and the self-learning torque value is less than an upper limit value, it is determined to be a first self-learning state; if the engine control torque deviation is less than or equal to a second deviation threshold and the self-learning torque value is greater than a lower limit value, it is determined to be a second self-learning state; if the engine control torque deviation is less than the first deviation threshold, or the engine control torque deviation is greater than the second deviation threshold, or the self-learning torque value is equal to the upper limit value or the lower limit value, it is determined to be a third self-learning state; if it is a first self-learning state, the self-learning torque value is increased at a preset rate; if it is a second self-learning state, the self-learning torque value is decreased at a preset rate; if it is a third self-learning state, the current self-learning torque value of the self-learning region is maintained; and the corresponding self-learning torque value is updated in real time in the storage unit of the self-learning region.

[0098] Figure 13 This is a calculation logic diagram of the self-learning state in this invention, used to illustrate the judgment process of the self-learning state. If the self-learning write-allow flag is set, and the torque deviation C is greater than or equal to the torque threshold Tq4 and the self-learning value is less than the upper limit TqUp, the self-learning state is 1 (first self-learning state); if the self-learning write-allow flag is set, and the torque deviation C ≤ threshold Tq5 and the self-learning value is greater than the lower limit TqDw, the self-learning state is 2 (second self-learning state); in all other cases, the self-learning state is 0 (third self-learning state).

[0099] Figure 14 This is the logic diagram for calculating the self-learning value when the self-learning state is 1 in this invention. Figure 15This is a logic diagram for calculating the self-learning value when the self-learning state is 2 in this invention, used to illustrate the self-learning torque calculation process in different states: the initial value of the self-learning value is 0; if the self-learning state is 1, as... Figure 14 As shown, the self-learning value increases at an increasing rate UpRate; if the self-learning state is 2, as... Figure 15 As shown, the self-learning value decreases at a decreasing rate DwRate; if the self-learning state is 0, the self-learning value remains unchanged from the previous moment; the self-learning value is updated to the storage unit in real time and will not be erased due to power-on or power-off conditions.

[0100] Optionally, after the torque value is obtained through self-learning calculation, a self-learning value reading step can also be performed, which includes self-learning read enable judgment and self-learning read value calculation.

[0101] Figure 16 This is a calculation logic diagram of the self-learning value reading flag in this invention, used to illustrate the process of enabling self-learning reading. If the engine start-up completion flag is set, and after a delay time T8, if the steady-state selection switch SW1 equals 0, then the self-learning reading flag is set; if the engine start-up completion flag is set, and after a delay time T8, if the steady-state selection switch SW1 equals 1 and the engine target torque stabilization flag is set, then the self-learning reading flag is set; otherwise, the self-learning reading flag is reset.

[0102] Figure 17 This is a logic diagram illustrating the calculation process of the self-learning read value in this invention. If the self-learning read flag is reset, the self-learning read value is 0; if the self-learning read flag is 1, the self-learning read value is obtained by low-pass filtering (filtering time is T9) of the self-learning values ​​of each storage unit.

[0103] In this embodiment, calibrating the engine signal torque based on the engine demand torque includes: running the hybrid vehicle's engine according to the engine demand torque and detecting the engine's actual torque, determining the actual torque as the engine's calculated torque; and subtracting the self-learned torque value from the engine's calculated torque to obtain the engine signal torque.

[0104] Figure 18 This is a flowchart of an embodiment of the present invention, providing an engine torque self-learning method. The engine controller acquires the engine target torque and generator signal torque via CAN communication, performs torque self-learning based on the torque deviation, and adjusts the engine's required torque to ensure that the engine signal torque matches the generator signal torque and the engine target torque. The process includes:

[0105] S1. Torque deviation calculation;

[0106] S2. Self-learning write;

[0107] Optionally, step S2 includes three parts: S21. Self-learning region division; S22. Self-learning write enable; S23. Self-learning torque calculation;

[0108] S3. Self-learning read;

[0109] Optionally, step S3 includes two parts: S31. Self-learning read enable determination; S32. Self-learning read value calculation;

[0110] S4. Calculation of engine torque requirements;

[0111] For step S4, engine torque demand calculation, the calculation logic is: engine torque demand equals engine target torque plus self-learning read value;

[0112] S5. Calculation of engine torque signal.

[0113] For step S5, engine torque signal calculation, the calculation logic is: engine signal torque equals engine calculated torque minus self-learned reading value.

[0114] The solution in this embodiment uses the generator signal torque as a verification benchmark for engine torque self-learning. Once the self-learning enabling conditions are met, torque deviation learning can be performed automatically, and the required engine torque can be adjusted automatically. This ensures that the values ​​of the engine target torque, engine signal torque, and generator signal torque are close, improving engine torque accuracy. Improving torque accuracy can improve fuel consumption performance, enhance low-temperature driving performance, and make it easier to balance battery power.

[0115] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0116] Example 2

[0117] This embodiment also provides an engine torque calibration device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementations, or a combination of software and hardware, are also possible and contemplated.

[0118] Figure 19 This is a structural block diagram of an engine torque calibration device according to an embodiment of the present invention, such as... Figure 19 As shown, the device includes:

[0119] The acquisition module 190 is used to acquire the target torque of the engine and the signal torque of the generator of the hybrid vehicle;

[0120] The calculation module 192 is used to calculate the torque deviation of the hybrid vehicle based on the engine target torque and the generator signal torque;

[0121] The calibration module 194 is used to calculate the engine demand torque of the hybrid vehicle based on the torque deviation through self-learning, and to calibrate the engine signal torque based on the engine demand torque so that the engine signal torque, the generator signal torque, and the engine target torque of the hybrid vehicle are consistent.

[0122] Optionally, the calculation module includes: a first calculation unit, used to obtain the engine target torque deviation by subtracting the historical engine signal torque of the previous control cycle from the engine target torque; a second calculation unit, used to obtain the engine actual torque deviation by subtracting the generator signal torque from the historical engine signal torque of the previous control cycle; and a third calculation unit, used to obtain the calculated engine control torque deviation by subtracting the generator signal torque from the engine target torque; wherein the torque deviation includes: the engine target torque deviation, the engine actual torque deviation, and the engine control torque deviation.

[0123] Optionally, the calibration module includes: a positioning unit, configured to locate a self-learning region based on the current engine parameters of the hybrid vehicle; a judgment unit, configured to determine whether it is permissible to write a self-learning torque value in the self-learning region based on the engine target torque deviation and the engine actual torque deviation; an execution unit, configured to, if it is permissible to write a self-learning torque value in the self-learning region, perform self-learning torque calculation in the self-learning region based on the engine control torque deviation, and store the self-learning torque value in a corresponding storage unit; and a first calibration unit, configured to read the self-learning torque value from the storage unit, and obtain the engine required torque by adding the engine target torque to the self-learning torque value.

[0124] Optionally, the positioning unit includes: a setting subunit for setting parameter thresholds for multiple engine operating parameters, wherein the parameter thresholds include X engine speed thresholds, Y engine load thresholds, and Z engine temperature thresholds, where X, Y, and Z are all positive integers greater than 0; a partitioning subunit for partitioning the number of engine storage units using the number of parameter thresholds, wherein each storage unit is used to store a self-learning value; an acquisition subunit for acquiring the current engine parameters of the hybrid vehicle; a comparison subunit for obtaining a parameter index by comparing the current engine parameters with the corresponding parameter thresholds, wherein the parameter index includes an engine speed index, an engine load index, and an engine temperature index, and the parameter index is used to characterize the threshold range in which the current engine parameters are located; and a positioning subunit for locating the storage unit number of the self-learning region using the parameter index.

[0125] Optionally, the partitioning subunit is further configured to: divide the engine operating area into (X+1)×(Y+1) areas; and divide the number of engine storage units into (X+1)×(Y+1)×Z units.

[0126] Optionally, the positioning subunit is also used to calculate the storage unit number i using the following formula: i = engine speed index × Z + engine load index × (X+1) × Z + engine temperature index.

[0127] Optionally, the determination unit includes: a first determining subunit, used to determine the stability indicators of the hybrid vehicle, wherein the stability indicators include: an engine target torque stability indicator, an engine speed stability indicator, and a generator torque stability indicator; a determining subunit, used to determine whether all the stability indicators are in a set state, and to determine whether the real-time operating condition of the hybrid vehicle meets all of the following preset conditions: engine coolant temperature is greater than a set value; engine start-up is completed and lasts for a certain period of time; engine is in torque control mode; the absolute value of the engine target torque deviation is less than or equal to a first threshold; the absolute value of the engine actual torque deviation is greater than or equal to a second threshold; and the engine target torque is greater than or equal to a third threshold; a second determining subunit, used to determine that self-learning writing is allowed if all the stability indicators are in a set state and the real-time operating condition of the hybrid vehicle meets all preset conditions.

[0128] Optionally, the execution unit includes: a processing subunit, configured to determine whether the engine control torque deviation is greater than or equal to a deviation threshold, and to compare the self-learning torque value of the self-learning region with a threshold value; a determining subunit, configured to determine a first self-learning state if the engine control torque deviation is greater than or equal to a first deviation threshold and the self-learning torque value is less than an upper limit value; determine a second self-learning state if the engine control torque deviation is less than or equal to a second deviation threshold and the self-learning torque value is greater than a lower limit value; determine a third self-learning state if the engine control torque deviation is less than the first deviation threshold, or the engine control torque deviation is greater than the second deviation threshold, or the self-learning torque value is equal to the upper or lower limit value; a learning subunit, configured to increase the self-learning torque value at a preset rate if it is the first self-learning state; decrease the self-learning torque value at a preset rate if it is the second self-learning state; and maintain the current self-learning torque value of the self-learning region if it is the third self-learning state; and an updating subunit, configured to update the corresponding self-learning torque value in the storage unit of the self-learning region in real time.

[0129] Optionally, the calibration module includes: a running unit, used to run the engine of the hybrid vehicle according to the engine demand torque, and detect the actual torque of the engine, and determine the actual torque as the engine calculated torque; and a second calibration unit, used to subtract the self-learning torque value from the engine calculated torque to obtain the engine signal torque.

[0130] According to one aspect of the embodiments of this application, a vehicle controller is also provided. Figure 20 This is a schematic diagram of a vehicle controller in an embodiment of the present invention, including: a generator control unit for transmitting generator signal torque to the engine control unit; an engine control unit for receiving the generator signal torque transmitted by the generator control unit and receiving the engine target torque transmitted by the vehicle control unit, including the engine torque calibration device in the above embodiment; and a vehicle control unit for receiving the engine signal torque transmitted by the engine control unit.

[0131] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0132] Example 3

[0133] Embodiments of the present invention also provide a storage medium storing a computer program, wherein the computer program is configured to execute the steps in any of the above method embodiments when running.

[0134] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0135] S1, obtain the target torque of the engine and the signal torque of the generator of the hybrid vehicle;

[0136] S2, calculate the torque deviation of the hybrid vehicle based on the engine target torque and the generator signal torque;

[0137] S3, calculate the engine demand torque of the hybrid vehicle based on the torque deviation self-learning, and calibrate the engine signal torque based on the engine demand torque to make the engine signal torque, the generator signal torque, and the engine target torque of the hybrid vehicle consistent.

[0138] Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0139] Embodiments of the present invention also provide an electronic device including a memory and a processor, the memory storing a computer program and the processor being configured to run the computer program to perform the steps in any of the above method embodiments.

[0140] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0141] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0142] S1, obtain the target torque of the engine and the signal torque of the generator of the hybrid vehicle;

[0143] S2, calculate the torque deviation of the hybrid vehicle based on the engine target torque and the generator signal torque;

[0144] S3, calculate the engine demand torque of the hybrid vehicle based on the torque deviation self-learning, and calibrate the engine signal torque based on the engine demand torque to make the engine signal torque, the generator signal torque, and the engine target torque of the hybrid vehicle consistent.

[0145] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0146] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0147] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0148] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0149] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0150] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0151] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0152] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method of calibrating engine torque, characterized by, The method comprises: acquiring an engine target torque and a generator signal torque of a hybrid vehicle; calculating a torque deviation of the hybrid vehicle according to the engine target torque and the generator signal torque; calculating an engine demand torque of the hybrid vehicle based on self-learning of the torque deviation, and calibrating an engine signal torque based on the engine demand torque, so that the engine signal torque, the generator signal torque and the engine target torque of the hybrid vehicle are consistent.

2. The method of claim 1, wherein, The calculating of the torque deviation of the hybrid vehicle according to the engine target torque and the generator signal torque comprises: subtracting a historical engine signal torque of a previous control period from the engine target torque to obtain an engine target torque deviation; subtracting the generator signal torque from the historical engine signal torque of the previous control period to obtain an engine real torque deviation; subtracting the generator signal torque from the engine target torque to obtain a calculated engine control torque deviation; wherein the torque deviation comprises the engine target torque deviation, the engine real torque deviation and the engine control torque deviation.

3. The method of claim 1, wherein, The calculating of the engine demand torque of the hybrid vehicle based on self-learning of the torque deviation comprises: locating a self-learning region according to current engine parameters of the hybrid vehicle; judging whether to allow writing of a self-learning torque value in the self-learning region based on the engine target torque deviation and the engine real torque deviation; if the writing of the self-learning torque value in the self-learning region is allowed, performing self-learning torque calculation in the self-learning region based on the engine control torque deviation, and storing the self-learning torque value in a corresponding storage unit; reading the self-learning torque value from the storage unit, and obtaining the engine demand torque by adding the engine target torque and the self-learning torque value.

4. The method of claim 3, wherein, The locating of the self-learning region according to the current engine parameters of the hybrid vehicle comprises: setting parameter thresholds of a plurality of engine operating parameters, wherein the parameter thresholds comprise X engine speed thresholds, Y engine load thresholds and Z engine temperature thresholds, X, Y and Z are positive integers greater than 0; dividing the number of storage units of the engine by the number of the parameter thresholds, wherein each storage unit is used to store a self-learning value; acquiring current engine parameters of the hybrid vehicle; comparing the current engine parameters with corresponding parameter thresholds to obtain parameter indexes, wherein the parameter indexes comprise engine speed indexes, engine load indexes and engine temperature indexes, and the parameter indexes are used to represent the threshold interval in which the current engine parameters are located; locating the storage unit number of the self-learning region by using the parameter indexes.

5. The method of claim 4, wherein, The dividing of the number of storage units of the engine by the number of the parameter thresholds comprises: dividing an engine operating region into (X+1)×(Y+1) regions; dividing the number of storage units of the engine into (X+1)×(Y+1)×Z.

6. The method of claim 4, wherein, The locating of the storage unit number of the self-learning region by using the parameter indexes comprises: calculating the storage unit number i by using the following formula: i = engine speed index * Z + engine load index * (X + 1) * Z + engine temperature index.

7. The method of claim 3, wherein, The determining whether to allow writing of the self-learning torque value in the self-learning area based on the engine target torque deviation and the engine real torque deviation comprises: determining a stability flag of the hybrid vehicle, wherein the stability flag comprises: an engine target torque stability flag, an engine speed stability flag, and a generator torque stability flag; determining whether the stability flag is in a set state, and determining whether a real-time working condition of the hybrid vehicle satisfies all preset conditions: the engine water temperature is greater than a set value; the engine starting is completed and lasts for a certain time; the engine is in a torque control mode; an absolute value of the engine target torque deviation is less than or equal to a first threshold value; an absolute value of the engine real torque deviation is greater than or equal to a second threshold value; the engine target torque is greater than or equal to a third threshold value; if the stability flag is in the set state and the real-time working condition of the hybrid vehicle satisfies all the preset conditions, it is determined that the self-learning writing is allowed.

8. The method of claim 3, wherein, The performing of the self-learning torque calculation in the self-learning area based on the engine control torque deviation and the storing of the self-learning value in the corresponding storage unit comprise: determining whether the engine control torque deviation is greater than or equal to a deviation threshold value, and comparing the self-learning torque value in the self-learning area with a threshold value; if the engine control torque deviation is greater than or equal to the first deviation threshold value and the self-learning torque value is less than an upper limit value, it is determined that the first self-learning state is reached; if the engine control torque deviation is less than or equal to the second deviation threshold value and the self-learning torque value is greater than a lower limit value, it is determined that the second self-learning state is reached; if the engine control torque deviation is less than the first deviation threshold value, or the engine control torque deviation is greater than the second deviation threshold value, or the self-learning torque value is equal to the upper limit value or the lower limit value, it is determined that the third self-learning state is reached; if the first self-learning state is reached, the self-learning torque value is increased at a preset rate; if the second self-learning state is reached, the self-learning torque value is decreased at a preset rate; if the third self-learning state is reached, the current self-learning torque value in the self-learning area is maintained; the corresponding self-learning torque value in the storage unit of the self-learning area is updated in real time.

9. The method of claim 1, wherein, The calibrating of the engine signal torque based on the engine demand torque comprises: running the engine of the hybrid vehicle according to the engine demand torque, and detecting a real torque of the engine, wherein the real torque is determined as an engine calculation torque; subtracting the self-learning torque value from the engine calculation torque to obtain the engine signal torque.

10. An engine torque calibration device, characterized by, The method comprises: an acquisition module, configured to acquire an engine target torque and a generator signal torque of a hybrid vehicle; a calculation module, configured to calculate a torque deviation of the hybrid vehicle according to the engine target torque and the generator signal torque; a calibration module, configured to self-learn to calculate an engine demand torque of the hybrid vehicle based on the torque deviation, and to calibrate an engine signal torque based on the engine demand torque, so that the engine signal torque, the generator signal torque, and the engine target torque of the hybrid vehicle are consistent.

11. A vehicle controller characterized by comprising: The method comprises: a generator control unit configured to transmit a generator signal torque to the engine control unit; an engine control unit configured to receive the generator signal torque transmitted by the generator control unit and to receive an engine target torque transmitted by the vehicle control unit and to perform the method according to any one of claims 1 to 9; a vehicle control unit configured to receive the engine signal torque transmitted by the engine control unit.

12. A storage medium, characterized by a computer program stored in the storage medium, wherein the computer program is configured to perform the method according to any one of claims 1 to 9 when executed. 13.An electronic device comprising a memory and a processor, the electronic device characterized by, a computer program stored in the memory, wherein the processor is configured to execute the computer program to perform the method according to any one of claims 1 to 9.

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