Learning compensation control method for motor torque deviation by hybrid electric vehicle engine
By obtaining the deviation value of the expected torque of the motor and the actual torque of the engine for learning and storing, and torque compensation is performed based on the engine operating conditions and temperature, the problem of torque deviation of the hybrid vehicle engine is solved, and the stability and precise control of torque learning are achieved, adapting to the Complex working conditions and environment.
Patent Information
- Application Number
- CN202411209665.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-30
- Publication Date
- 2025-01-07
AI Technical Summary
The torque deviation caused by engine dispersion and environmental changes in hybrid vehicle engines under complex working conditions and environments leads to a difference between the hybrid control unit's motor power generation torque demand and the actual torque output of the engine, affecting the accuracy of power control.
By obtaining the deviation value of the motor's expected torque and the actual engine torque, calculating the deviation gain, gradually accumulating the torque learning value and storing it, torque compensation is performed based on the engine operating conditions and intake temperature, limiting learning and storage conditions to prevent misleading and ensuring torque compensation stability and accuracy.
It realizes stability and precise compensation for torque learning, adapts to different working conditions and environments, ensures smooth engine operation, and avoids the impact of torque deviation on the power system after restart.
Smart Images
Figure CN119261860A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of hybrid vehicle engine electronic control, and in particular to a compensation control method for hybrid vehicle engine actual torque to motor desired torque deviation learning. Background Art
[0002] Due to the complex operating conditions and environment of the whole vehicle, the engine needs to adapt to the requirements of various motor torques and torque accuracy in various complex environments. The accurate engine torque is calibrated on the test bench. Due to the engine dispersion and different environments, such as high temperature, high altitude, low temperature, etc., the actual engine friction torque and pumping loss will always deviate from the calibration value on the whole vehicle. As a result, when the hybrid vehicle generates electricity, there is always a gap between the hybrid control unit (HCU)'s demand for motor power generation torque and the actual torque output of the engine. The engine must learn this torque difference and compensate it to the engine execution torque to adapt to the precise control of torque by the hybrid vehicle. Summary of the invention
[0003] The purpose of the present invention is to overcome the defects of the prior art and provide a hybrid vehicle engine to motor torque deviation learning compensation control method.
[0004] The purpose of the present invention can be achieved by the following technical solutions:
[0005] A hybrid vehicle engine to motor torque deviation learning compensation control method, the steps comprising:
[0006] Obtaining a deviation value between the expected power generation torque of the motor and the torque actually executed by the engine, or a deviation value between the expected power generation torque of the motor and the actual power generation torque of the motor;
[0007] When the torque learning condition is met, the deviation gain corresponding to the deviation value is obtained, and the deviation value is multiplied by the deviation gain to obtain the current torque learning step value;
[0008] When the storage condition is met, a new torque storage value is obtained by adding the product of the current torque learning value and the storage gain to the current torque storage value, and the torque storage value is stored based on the current engine operating condition and the intake air temperature;
[0009] Based on the current torque learning value and the torque storage value, the total compensation of the engine to the motor torque is added to the internal torque of the engine.
[0010] As a preferred technical solution, the deviation value between the desired generator torque and the actual engine torque is specifically the difference between the generator torque requirement of the hybrid power control unit and the actual engine torque.
[0011] As a preferred technical solution, the deviation value between the desired generator torque and the actual engine torque can also be specifically selected as the difference between the generator torque requirement of the hybrid control unit and the actual torque executed by the generator.
[0012] As a preferred technical solution, the method establishes a correspondence table between deviation values and deviation gains, and obtains the corresponding deviation gain by looking up the table with the deviation value as the axis.
[0013] As a preferred technical solution, the conditions for performing the torque learning include but are not limited to:
[0014] There are no engine torque-related faults; the engine execution torque is stable, and there is no need to de-ignite the ignition angle due to torque execution requirements; the engine start time is greater than the set threshold; the engine speed is stable; the HCU requests the engine torque to be stable; the hybrid system has no faults; the power battery stored electricity SOC is lower than the set threshold; the above learning conditions are all met, and the stabilization time is greater than the set stabilization time threshold.
[0015] As a preferred technical solution, the method sets a calibratable step threshold for limiting a single torque learning value.
[0016] As a preferred technical solution, the method sets a calibratable maximum value and a minimum value for the accumulated value of the torque learning value to limit it.
[0017] As a preferred technical solution, the storage conditions of the learning value include but are not limited to: the engine speed is higher than the limit; the engine speed change rate does not exceed the limit; the engine cooling water temperature is within the set range; the engine is not in the specified diagnostic operating condition.
[0018] As a preferred technical solution, the method is divided into multiple different temperature intervals based on the engine intake temperature, and each temperature interval is divided into multiple operating condition blocks according to the engine speed and load; the compensation torque storage value is stored in the corresponding operating condition block based on the current intake temperature and the engine speed and load.
[0019] As a preferred technical solution, the method sets a calibratable step threshold value to limit the torque learning value accumulated and stored once.
[0020] As a preferred technical solution, the method sets a calibratable maximum value and a minimum value of the accumulated value of the total torque learning value to limit it.
[0021] As a preferred technical solution, the method directly reads the compensation torque storage value when the system is initialized.
[0022] Compared with the prior art, the present invention has the following beneficial effects:
[0023] 1) The present invention proposes a slow learning scheme, which takes the deviation between the motor power generation torque demand and the actual torque executed by the engine as the axis of the learning step length and limits the step length of a single learning, and accumulates the torque learning values that meet the learning value storage conditions. Through the fluctuation of the two, the deviation of the engine execution torque is steadily compensated. Compared with the fast learning compensation scheme that pursues one-step completion, the torque learning is more stable, the compensation for the engine torque deviation is more reasonable, and the engine works more smoothly.
[0024] 2) The present invention strictly limits the conditions for the engine's error learning of the power generation torque to prevent mislearning, and strictly limits the maximum and minimum values of the learned torque step and the stored torque step. The total learning value and the stored learning value are also simultaneously limited to the maximum and minimum values to prevent excessive learning deviations. The engine's various blocks and various intake temperatures are distinguished for storage and reading, and the learning accuracy is further improved, with better adaptability.
[0025] 3) The present invention stores the learning values according to the operating ranges to which the intake air temperature, engine speed and load belong, respectively, so that the torque control adjustment is more in line with the actual operating conditions and environmental conditions of the engine; and after the system is restarted, the stored values can be directly read and applied to the engine torque deviation compensation, thus avoiding the influence of the torque deviation during the period before the learning is completed after the restart on the power system. BRIEF DESCRIPTION OF THE DRAWINGS
[0026] Figure 1 The present invention is a flow chart of a hybrid vehicle engine to motor torque deviation learning and compensation control method. DETAILED DESCRIPTION
[0027] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.
[0028] Example 1
[0029] The present invention proposes a hybrid electric vehicle engine-to-motor torque deviation learning compensation control method based on learning the difference between the motor power generation torque demand and the actual engine torque, and compensating to the engine execution torque, so as to adapt to the precise control of the torque of the hybrid vehicle. The specific steps are as follows: Figure 1 As shown:
[0030] S1. Obtaining a deviation value between the expected power generation torque of the motor and the actual execution torque of the engine, or the actual power generation torque of the generator;
[0031] S2. When the torque learning condition is met, the deviation gain corresponding to the deviation value is obtained, the deviation value is multiplied by the deviation gain to obtain the current torque learning step value, and the cumulative step length of the current torque learning is calculated;
[0032] S3. When the storage condition is met, the current torque storage value is added with the product of the current torque learning value and the storage gain, and a new torque storage value is gradually accumulated, and the torque storage value is stored based on the current engine operating condition and the intake air temperature range;
[0033] S4. Based on the current torque learning value and the torque storage value in the interval corresponding to the current engine operating condition and the intake air temperature, a total compensation for the engine torque to the motor is obtained and added into the calculation of the internal torque of the engine.
[0034] The details of each part are as follows:
[0035] 1. Learning basis
[0036] When there is a deviation between the desired torque of the generator and the torque executed by the engine, or when there is a deviation between the actual torque executed by the generator and the torque required by the HCU, this deviation value is used as the axis of the learning step to obtain the deviation gain by looking up the table.
[0037] Table 1: Correspondence between deviation value and deviation gain
[0038]
[0039] 2. Learning conditions
[0040] This gain is subject to learning conditions, and will not be executed if the learning conditions are not met. The learning conditions include but are not limited to: 1) There are no engine torque-related faults, 2) The engine execution torque is stable, and there is no need to de-ignite the ignition angle due to torque execution requirements, 3) The engine start time needs to be greater than a certain value, 4) The engine speed needs to be stable, 5) The HCU requests the engine torque to be stable, 6) The hybrid system including the motor, battery, etc. has no faults, 7) The power battery stored electricity SOC cannot be too high, 8) The above learning conditions are met and need to be stable for a period of time.
[0041] 3. Learning process
[0042] The gain multiplied by the deviation is used as the torque learning value, i.e., the learning step, which will be limited by the calibrable step; the step will be accumulated, and its accumulated value will be limited by the calibrable maximum and minimum values to obtain the limited torque learning value.
[0043] a Storage conditions of learning values
[0044] The following conditions are met (not limited to): 1) the engine speed is lower than the limit, 2) the engine speed change rate cannot exceed the limit, 3) the engine cooling water temperature needs to be within a certain range, 4) the engine cannot be in some of the set diagnostic conditions.
[0045] bStored Procedure
[0046] The torque learning value after the restriction under the storage condition is accumulated after restriction to obtain the compensation torque storage value. For the torque learning value stored in a single accumulation, a calibratable step threshold is set to limit it; for the accumulated value of the total torque learning value, a calibratable accumulated value maximum value and minimum value are set to limit it.
[0047] This stored value will be directly read when the system is initialized to reduce the repeated learning process and speed up the torque compensation learning adjustment. 4. Division of learning blocks
[0048] The engine operating conditions are divided into 20 blocks according to speed and load. Since the engine torque accuracy is greatly affected by the intake temperature, these 20 blocks are divided into 4 different temperature areas according to different intake temperature (or ambient temperature) intervals. Therefore, the engine storage area will have 80 intervals, which is more accurate for torque control adjustment. For example, in the following table, RPM1 / 2 / 3 / 4 / 5 is the engine speed interval, IMEP1 / 2 / 3 / 4 is the engine load interval, and IAT1 / 2 / 3 / 4 is the engine operation.
[0049] Table 2: Engine compensation torque storage range
[0050]
[0051]
[0052] 5. Torque compensation
[0053] The total torque compensation = current torque learning value + torque storage value of the current block. The total torque compensation will be added to the engine's internal torque calculation.
[0054] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.
Claims
1. A hybrid vehicle engine to motor torque deviation learning compensation control method, characterized in that the steps include: Obtaining a deviation value between the motor's expected power generation torque and the torque actually executed by the engine, or a deviation value between the motor's expected torque and the actual executed torque; When the torque learning condition is met, the deviation gain corresponding to the deviation value is obtained, and the deviation value is multiplied by the deviation gain to obtain the current torque learning step length; When the storage condition is met, the current torque learning value is multiplied by the storage gain based on the above learning step length, and the accumulated storage value before the current operating condition block is added to obtain a new torque storage value, and the torque storage value is stored based on the current operating condition block of the engine; Based on the current torque learning value and the torque storage value, the total compensation of the engine to the motor torque is added to the engine internal torque calculation.
2. A hybrid vehicle engine to motor torque deviation learning compensation control method according to claim 1, characterized in that: The method establishes a corresponding table of deviation values and deviation gains, and obtains the corresponding deviation gain by looking up the table with the deviation value as the axis.
3. A hybrid vehicle engine to motor torque deviation learning compensation control method according to claim 1, characterized in that: The conditions for performing the torque learning include but are not limited to: There are no engine torque-related faults; the engine execution torque is stable, and there is no need to de-ignite the ignition angle due to torque execution requirements; the engine start time is greater than the set threshold; the engine speed is stable; the HCU requests the engine torque to be stable; the hybrid system has no faults; the power battery stored electricity SOC is lower than the set threshold; the above learning conditions are all met, and the stabilization time is greater than the set stabilization time threshold.
4. A hybrid vehicle engine to motor torque deviation learning compensation control method according to claim 1, characterized in that: The method sets a calibratable step threshold for a single torque learning value to limit it.
5. A hybrid vehicle engine to motor torque deviation learning compensation control method according to claim 1, characterized in that: The method sets a maximum value and a minimum value of the accumulated value that can be calibrated to limit the accumulated value of the torque learning value.
6. A hybrid vehicle engine to motor torque deviation learning compensation control method according to claim 1, characterized in that: The storage conditions of the learning value include but are not limited to: the engine speed is higher than the limit; the engine speed change rate does not exceed the limit; the engine cooling water temperature is within the set range; the engine is not in the specified diagnostic operating condition.
7. A hybrid vehicle engine to motor torque deviation value learning compensation control method according to claim 6, characterized in that: The method limits a calibrable step threshold for setting a single torque learning storage step.
8. A hybrid vehicle engine to motor torque deviation value learning compensation control method according to claim 6, characterized in that: The method sets a maximum value and a minimum value of the accumulated value that can be calibrated to limit the accumulated value of the stored learning value.
9. A hybrid vehicle engine to motor torque deviation learning compensation control method according to claim 1, characterized in that: The method is based on dividing the engine intake temperature into a plurality of different temperature intervals, and each temperature interval is divided into a plurality of operating condition blocks according to the engine speed and load; The compensation torque storage value is stored in the corresponding operating condition block based on the current intake air temperature and the engine speed and load.
10. A hybrid vehicle engine to motor torque deviation learning compensation control method according to claim 1, characterized in that: The method directly reads the compensation torque storage value when the system is initialized.