Hydraulic retarder braking torque self-learning compensation control system and method

Abstract

The application discloses a hydraulic retarder braking torque self-learning compensation control system and method, relates to the field of hydraulic retarder braking torque control, and comprises the following steps: a retarder control unit acquires a driver-requested braking torque, an actual braking torque, a rotor rotating speed, a working oil pressure and an oil temperature in real time; a pre-stored viscosity correction coefficient is called according to the current oil temperature, a dynamic correction is made on a rated output braking torque, and a target braking torque adapted to the current oil viscosity is obtained; the actual braking torque and the target braking torque are compared in real time, and a torque deviation is calculated; when the absolute value of the deviation exceeds a preset threshold, torque compensation is started; a proportional valve current compensation amount is calculated according to the torque deviation, an actual driving current is output to control the opening degree of the proportional valve, the working cavity oil pressure is adjusted by a master control valve, and the torque deviation is compensated. According to the application, the influence of oil viscosity on the braking torque is dynamically corrected according to the oil temperature, the output current of the proportional valve is dynamically adjusted, and the closed-loop compensation of the braking torque is realized.

Description

Technical Field

[0001] This invention relates to the field of hydraulic retarder braking torque control, and more particularly to a hydraulic retarder braking torque self-learning compensation control system and a hydraulic retarder braking torque self-learning compensation control method. Background Technology

[0002] The braking torque output of a hydraulic retarder depends on the hydraulic damping of the oil in the working chamber, and the oil viscosity directly determines the damping effect. As oil temperature increases, oil viscosity decreases, resulting in a lower braking torque for the same control current. Conversely, as oil temperature decreases, viscosity increases, leading to a higher braking torque.

[0003] In existing technologies, the braking torque control of retarders mostly adopts a fixed MAP lookup calibration table, simply matching the speed and braking request without dynamically compensating for oil viscosity fluctuations or considering issues such as oil deterioration over long-term use.

[0004] Therefore, it can easily lead to problems such as low braking torque accuracy, poor braking smoothness, and insufficient adaptability.

[0005] To address the aforementioned shortcomings, this invention proposes a braking torque compensation control method that simplifies parameters and focuses on viscosity characteristics, thus resolving the pain points of existing technologies. Summary of the Invention

[0006] The purpose of this invention is to provide a self-learning compensation control system and a self-learning compensation control method for braking torque of a hydraulic retarder, thereby solving at least one of a number of technical problems.

[0007] Core technical issues include low braking torque accuracy, poor braking smoothness, and insufficient adaptability of the retarder braking system.

[0008] This invention provides the following solution: According to a first aspect of the present invention, a self-learning compensation control method for braking torque of a hydraulic retarder is provided, comprising: Real-time acquisition of requested braking torque, actual braking torque, and fluid temperature; In response to the requested braking torque, the rated output braking torque is dynamically corrected based on the current oil temperature and the pre-stored viscosity correction coefficient, so as to obtain the target braking torque that is adapted to the current oil viscosity. The actual braking torque is compared with the target braking torque in real time to calculate the torque deviation; when the absolute value of the torque deviation exceeds the preset threshold, torque compensation is initiated. Calculate the compensation torque deviation based on the torque deviation; The system continuously collects signals and dynamically updates the deviation and compensation amount, keeping the deviation between the actual braking torque and the target braking torque within a preset accuracy range.

[0009] Furthermore, it also includes a self-learning step for the viscosity correction factor: Within the same oil temperature range and the same speed range, when the oil pressure fluctuation is within the set range, the retarder is fault-free, the effective working condition of continuously stable braking torque is accumulated to a preset number of times, and the average torque deviation of the range exceeds the threshold, self-learning is triggered. The correction amount of the viscosity correction coefficient is calculated based on the interval average torque deviation, the ratio of rated oil pressure to actual oil pressure, and the self-learning coefficient. Only the viscosity correction factor for the current oil temperature range is updated, while the viscosity correction factor for other temperature ranges remains unchanged; the magnitude of each update is limited to a preset threshold range, and the updated viscosity correction factor is stored.

[0010] Furthermore, including: The target braking torque Tq3 satisfies: Tq3 = Tq2 × k; Where Tq2 is the rated output braking torque under the current operating conditions, and k is the viscosity correction coefficient corresponding to the current oil temperature.

[0011] Furthermore, including: The proportional valve current compensation ΔI satisfies: ΔI=I1×(ΔTq / Tq3)×Ka; Where I1 is the current proportional valve base control current, ΔTq is the torque deviation, Tq3 is the target braking torque, and Ka is the compensation gain.

[0012] According to a second aspect of the present invention, a self-learning compensation control system for braking torque of a hydraulic retarder is provided for implementing a self-learning compensation control method for braking torque of a hydraulic retarder. The self-learning compensation control system for braking torque of a hydraulic retarder includes: The signal detection module includes an oil temperature sensor, a speed sensor, an oil pressure sensor, and a torque calculation unit, which are used to collect the oil temperature, rotor speed, and working oil pressure of the retarder, and output the calculated actual braking torque; and to acquire the requested braking torque in real time. The hydraulic control valve group, including the on / off valve, proportional valve, and main control valve, is connected to the oil circuit of the retarder working chamber and is used to adjust the oil pressure in the working chamber to change the output braking torque. The storage module is used to pre-store calibration parameters, including an oil temperature and viscosity correction coefficient table; The retarder control unit is connected to the signal detection module, the hydraulic control valve group, and the storage module respectively. The retarder control unit is used to respond to the requested braking torque. Based on the current oil temperature, it calls the pre-stored viscosity correction coefficient to dynamically correct the rated output braking torque and obtain the target braking torque that is adapted to the current oil viscosity. Then, based on the real-time comparison of the actual braking torque and the target braking torque, the torque deviation is calculated, the drive parameters of the hydraulic control valve group are adjusted in a closed loop, the viscosity correction coefficient of the current oil temperature range is updated, and the updated viscosity correction coefficient is stored.

[0013] Furthermore, it includes: a switching valve, a proportional valve, and a main control valve located between the main oil supply circuit of the retarder and the working chamber of the retarder; The proportional valve is used to continuously adjust the flow rate and oil pressure of the main oil circuit supplying the retarder through the PWM duty cycle. The main control valve is mechanically passively linked with the oil pressure in the working chamber. The switching valve is used to open and close the main oil circuit supplying the retarder.

[0014] Furthermore, the retarder control unit includes a feedforward correction unit, a closed-loop compensation unit, and a self-learning iteration unit; The feedforward correction unit is used to calculate the target braking torque that is adapted to the current viscosity by calling the viscosity correction coefficient according to the oil temperature. The closed-loop compensation unit is used to calculate the torque deviation based on the target braking torque and actual braking torque of the current year calculated by the feedforward correction unit, output the proportional valve current compensation amount, and drive the hydraulic control valve group to adjust the oil pressure. The self-learning iterative unit is used to accumulate data under steady-state effective operating conditions, calculate the average torque deviation based on the torque deviation calculated by the closed-loop compensation unit, and update the viscosity correction coefficient by temperature zone.

[0015] Furthermore, it includes: configuring a limiting protection subunit for the self-learning iterative unit to limit the update amplitude of a single viscosity correction coefficient to not exceed a preset threshold range; The self-learning iteration unit is also equipped with a reset interface, which supports clearing the viscosity correction factor table and restoring the factory initial value after oil replacement via a diagnostic tool.

[0016] Furthermore, the retarder control unit is equipped with an over-temperature shielding unit, which is used to shield the torque compensation and self-learning iteration process when the retarder enters the over-temperature torque limiting mode.

[0017] According to a third aspect of the present invention, an electronic device is provided, comprising: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus; The memory stores a computer program, which, when executed by the processor, causes the processor to perform steps such as the self-learning compensation control method for the braking torque of a hydraulic retarder.

[0018] The above solution achieves the following beneficial technical effects: This invention provides dynamic compensation for fluctuations in oil viscosity, taking into account the deterioration of oil over long-term use. Utilizing the existing controller, proportional valve, on / off valve, main control valve, oil pressure sensor, speed sensor, and oil temperature sensor of the retarder, it acquires parameters such as oil temperature, speed, oil pressure, and actual braking torque in real time. Combined with a pre-stored rated oil temperature-viscosity correction coefficient table, it dynamically corrects the impact of oil viscosity on braking torque based on oil temperature, and dynamically adjusts the output current of the proportional valve to achieve closed-loop compensation of braking torque. Attached Figure Description

[0019] Figure 1 This is a structural diagram of a hydraulic retarder braking torque self-learning compensation control system provided in one or more embodiments of the present invention.

[0020] Figure 2 This is a flowchart of a self-learning compensation control method for braking torque of a hydraulic retarder provided in one or more embodiments of the present invention.

[0021] Figure 3 This is a block diagram of an electronic device for a self-learning compensation control method for braking torque of a hydraulic retarder provided in one or more embodiments of the present invention. Detailed Implementation

[0022] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Figure 1 This is a structural diagram of a hydraulic retarder braking torque self-learning compensation control system provided in one or more embodiments of the present invention.

[0024] Example 1, as Figure 1 The hydraulic retarder braking torque self-learning compensation control system shown includes: The signal detection module includes an oil temperature sensor, a speed sensor, an oil pressure sensor, and a torque calculation unit, which are used to collect the oil temperature (retarder oil temperature T), rotor speed (retarder rotor speed n), and working oil pressure (real-time oil pressure P1 in the oil circuit), and output the actual braking torque (actual braking torque Tq1 of the retarder). The hydraulic control valve group, including the original design's on / off valve, proportional valve, and main control valve, is connected to the retarder's working chamber oil circuit to adjust the working chamber oil pressure to change the output braking torque. The storage module is used to pre-store calibration parameters, including the oil temperature viscosity correction coefficient table (KL_k), the speed-rated torque MAP table (KF_Tq2), the speed-proportional valve current MAP table (KF_I), and the speed-rated oil pressure MAP table (KF_P2). The retarder control unit is connected to the signal detection module, the hydraulic control valve group, and the storage module. The retarder control unit receives the driver's requested braking torque (Tqd), combines it with the current oil temperature, calls the viscosity correction coefficient to perform feedforward correction on the rated output braking torque to obtain the target braking torque, and then adjusts the drive parameters of the hydraulic control valve group in a closed loop according to the deviation between the actual braking torque and the target braking torque. Under steady-state conditions, it self-learns and updates the viscosity correction coefficient table in the storage module.

[0025] In this embodiment, in the hydraulic control valve group, the switching valve is connected in series in the main oil circuit to open and close the oil circuit, the proportional valve is used to continuously adjust the oil flow and oil pressure through the PWM duty cycle, and the main control valve is mechanically passively linked with the working chamber oil pressure.

[0026] In this embodiment, the retarder control unit includes a feedforward correction unit, a closed-loop compensation unit, and a self-learning iteration unit; The feedforward correction unit is used to call the viscosity correction coefficient based on the oil temperature to calculate the target braking torque that is adapted to the current viscosity; The closed-loop compensation unit is used to calculate torque deviation, output proportional valve current compensation, and drive the hydraulic control valve group to adjust oil pressure. The self-learning iterative unit is used to accumulate data under steady-state effective operating conditions, calculate the average torque deviation, and update the viscosity correction coefficient by temperature zone.

[0027] In this embodiment, the self-learning iteration unit is equipped with a limiting protection subunit to limit the update range of the viscosity correction coefficient in a single operation to no more than ±5%; it is also equipped with a reset interface to support clearing the viscosity correction coefficient table and restoring the factory initial value after oil replacement via a diagnostic instrument.

[0028] In this embodiment, the retarder control unit is equipped with an over-temperature shielding unit, which is used to shield the torque compensation and self-learning iteration process when the retarder enters the over-temperature torque limiting mode.

[0029] Figure 2 This is a flowchart of a self-learning compensation control method for braking torque of a hydraulic retarder provided in one or more embodiments of the present invention.

[0030] Example 2, as Figure 2 The hydraulic retarder braking torque self-learning compensation control method shown is implemented in a hydraulic retarder braking torque self-learning compensation control system. The hydraulic retarder braking torque self-learning compensation control method includes the following steps: T1 signal acquisition: The retarder control unit acquires the driver's requested braking torque, actual braking torque, rotor speed, working oil pressure and oil temperature in real time; T2 viscosity feedforward correction: Based on the current oil temperature, the pre-stored viscosity correction coefficient is called to dynamically correct the rated output braking torque to obtain the target braking torque that is adapted to the current oil viscosity. T3 Torque Deviation Judgment: Real-time comparison between actual braking torque and target braking torque to calculate torque deviation; when the absolute value of the deviation exceeds the preset threshold, torque compensation is activated. T4 closed-loop current compensation: Calculates the proportional valve current compensation amount based on the torque deviation, outputs the actual drive current to control the opening of the proportional valve, and links the main control valve to adjust the working chamber oil pressure to compensate for the torque deviation. T5 Cyclic Correction: Continuously acquires signals and dynamically updates deviations and compensation amounts to control the deviation between the actual braking torque and the target braking torque within a preset accuracy range.

[0031] In this embodiment, a viscosity correction coefficient self-learning step T6 is also included: T6.1 Trigger Judgment: Within the same oil temperature range and the same speed range, when the speed and oil pressure fluctuations are within the set range, the retarder is fault-free, the effective working condition of continuously stable braking torque is accumulated to the preset number of times, and the average torque deviation of the range exceeds the threshold, self-learning is triggered. T6.2 Correction Calculation: The correction amount of the viscosity correction coefficient is calculated based on the interval average torque deviation, the ratio of rated oil pressure to actual oil pressure, and the self-learning coefficient. T6.3 Coefficient Update: Only the viscosity correction coefficient for the current oil temperature range is updated, while the coefficients for other temperature ranges remain unchanged; the range of a single update is limited to ±5%, and the updated coefficients are stored in the storage module.

[0032] In this embodiment, the target braking torque satisfies: Tq3 = Tq2 × k, where Tq2 is the rated output braking torque under the current operating conditions, and k is the viscosity correction coefficient corresponding to the current oil temperature.

[0033] In this embodiment, the proportional valve current compensation amount satisfies: ΔI=I1×(ΔTq / Tq3)×Ka, where I1 is the current proportional valve base control current, ΔTq is the torque deviation, Tq3 is the target braking torque, and Ka is the compensation gain; when the torque deviation is positive, Ka takes a value of 1.0~1.1, and when the torque deviation is negative, Ka takes a value of 0.9~1.0.

[0034] In this embodiment, when the retarder enters the over-temperature torque limiting mode, the torque compensation control and self-learning process are shielded.

[0035] Specifically: Example 3, in one specific embodiment, discloses a braking torque self-learning compensation control method based on oil temperature and viscosity characteristics, applied to a hydraulic retarder system. The system includes a retarder control unit, a proportional valve, a switching valve, a main control valve, an oil pressure sensor, a speed sensor, and an oil temperature sensor. The method includes the following steps: Signal acquisition: The retarder control unit acquires in real time the driver's requested braking torque Tqd, the actual braking torque of the retarder Tq1, the real-time oil pressure P1 in the oil circuit, the retarder rotor speed n, and the retarder oil temperature T. Calibration parameter retrieval: Retrieve pre-stored calibration parameters, including oil temperature viscosity correction coefficient table KL_k, speed-requested braking torque corresponding to rated output braking torque MAP table KF_Tq2, speed-rated output braking torque corresponding to proportional valve control current MAP table KF_I, and speed-rated output braking torque corresponding to rated oil pressure MAP table KF_P2; Oil viscosity effect correction: Based on the current oil temperature T, the corresponding correction coefficient is retrieved from the oil temperature viscosity correction coefficient table KL_k, and the rated output braking torque Tq2 is dynamically corrected to obtain the theoretical rated output braking torque Tq3 that is adapted to the current oil viscosity. Braking torque deviation determination: The actual braking torque Tq1 is compared with the theoretical rated output braking torque Tq3 in real time, and the torque deviation ΔTq is calculated; when the absolute value of the torque deviation exceeds the preset deviation threshold, torque compensation is activated. Proportional valve current compensation output: The proportional valve current compensation amount is calculated based on the torque deviation to obtain the actual output current. The proportional valve is then driven by the PWM duty cycle to adjust the opening, and the main control valve is synchronously linked to adjust the working chamber oil pressure to compensate for the braking torque deviation. Self-learning update of correction coefficients: Accumulate operating data under steady-state effective conditions, calculate the average torque deviation of the corresponding oil temperature range, and update the correction coefficients of the corresponding temperature range in the oil temperature viscosity correction coefficient table KL_k accordingly.

[0036] In this embodiment, in the oil viscosity influence correction step, the theoretical rated output braking torque satisfies: Tq3=Tq2×k, where k is the viscosity correction coefficient corresponding to the current oil temperature.

[0037] In this embodiment, in the braking torque deviation determination step, the torque deviation satisfies ΔTq=Tq3. Tq1; The preset deviation threshold ΔTq0 is 5% of the current theoretical rated output braking torque Tq3; Torque abnormality is determined and compensation is initiated only when |ΔTq|>ΔTq0.

[0038] In this embodiment, in the proportional valve current compensation output step, the current compensation amount satisfies: ΔI=I1×(ΔTq / Tq3)×Ka, where I1 is the current proportional valve base control current and Ka is the compensation gain; when the torque deviation is positive, the value of Ka ranges from 1.0 to 1.1; when the torque deviation is negative, the value of Ka ranges from 0.9 to 1.0; the actual output current of the proportional valve is I2=I1+ΔI.

[0039] In this embodiment, the retarder control unit continuously collects real-time signals, dynamically updates the torque deviation and current compensation, and performs full-condition cyclic correction to control the deviation between the actual braking torque and the theoretical rated output braking torque within ±5%.

[0040] In this embodiment, the triggering condition for the self-learning update of the correction coefficients is: Within the same oil temperature range and the same speed range, the effective working conditions that meet the following requirements are accumulated to a preset number of times: the fluctuation range of speed and actual oil pressure does not exceed ±5% within a set time, the retarder is fault-free, and the driver requests a continuous and stable braking torque for ≥5 seconds. Furthermore, the absolute value of the actual average torque deviation within this range is greater than 5% of the theoretical rated output braking torque.

[0041] In this embodiment, the correction amount of the correction coefficient satisfies: Δk=(ΔTq_avg / Tq3)×(P2 / P1)×Kb, where ΔTq_avg is the actual average torque deviation in the current interval, P2 is the rated oil pressure under the current working condition, P1 is the current actual oil pressure, and Kb is the self-learning coefficient, with a value range of 0.8~1.0.

[0042] In this embodiment, when updating the oil temperature viscosity correction coefficient table, only the correction coefficient of the oil temperature range to which the current operating condition belongs is modified, while the coefficients of other temperature ranges remain unchanged; the updated correction coefficients satisfy: k_new=k_old×(1+Δk), and the updated coefficients are stored in the corresponding storage area of ​​the controller.

[0043] In this embodiment, the magnitude of a single correction coefficient update shall not exceed ±5%. If the correction exceeds this range, the original coefficient shall remain unchanged. After the retarder oil is replaced, the oil temperature and viscosity correction coefficient table can be manually reset using a diagnostic tool to restore it to the factory initial value and restart self-learning.

[0044] In this embodiment, when the retarder is in the over-temperature and over-torque limiting mode, the self-learning compensation control method is disabled and stops executing.

[0045] It is worth noting that although this system / device only discloses the above-mentioned modules / units, it does not mean that this system / device is limited to the above-mentioned basic functional modules. On the contrary, what this invention intends to express is that, based on the above-mentioned basic functional modules, those skilled in the art can add one or more functional modules in combination with the prior art to form an infinite number of embodiments or technical solutions. That is to say, this system is open rather than closed. It cannot be assumed that the scope of protection of the claims of this invention is limited to the above-disclosed basic functional modules just because this embodiment only discloses a few basic functional modules.

[0046] Example 4, in a specific embodiment, discloses a self-learning compensation control method for braking torque based on oil temperature and viscosity characteristics. This method utilizes existing retarder controllers, proportional valves, switching valves, main control valves, oil pressure sensors, speed sensors, and oil temperature sensors to acquire parameters such as oil temperature, speed, oil pressure, and actual braking torque in real time. Combined with a pre-stored rated oil temperature-viscosity correction coefficient table, the method dynamically adjusts the proportional valve output current based on the influence of oil viscosity on braking torque according to oil temperature, thereby achieving closed-loop compensation of the braking torque.

[0047] S1. The retarder control unit receives the following signals: (1) The driver requests braking torque Tqd; (2) Actual braking torque Tq1 of the retarder (3) Real-time oil pressure P1 in the oil passage; (4) The retarder rotor speed n; (5) Retarder oil temperature T.

[0048] S2. The retarder control unit obtains the following calibration parameters (when the product is off the production line, the controller can be flashed with parameters based on bench tests and actual vehicle calibration in the storage area; all parameters can be adjusted to adapt to different retarder models): (1) KL_k: Oil temperature viscosity correction coefficient table, calibrating the effect of viscosity on torque at different oil temperatures; (2) KF_Tq2: Speed ​​- Requested braking torque corresponding to rated output braking torque MAP table; (3) KF_I: MAP table of proportional valve control current corresponding to speed-rated output braking torque; (4) KF_P2: Speed-rated output braking torque corresponding to rated oil pressure MAP table.

[0049] S3, Correction for the effect of oil viscosity: The retarder control unit retrieves the corresponding correction coefficient from KL_k based on the current oil temperature T, and dynamically corrects the rated output braking torque to obtain the theoretical rated output braking torque Tq3=Tq2 adapted to the current oil viscosity. k.

[0050] S4. Calculation of retarder braking torque deviation: The retarder control unit compares the actual torque Tq1 with the theoretical rated output braking torque Tq3 in real time and calculates the deviation: △Tq=Tq3 Tq1. If the deviation is positive, the actual braking torque of the retarder is insufficient, and the proportional valve control current needs to be increased. If the deviation is negative, the actual torque of the retarder is excessive, and the proportional valve current needs to be decreased. Only when |ΔTq| > ΔTq0 (e.g., 5% of the current theoretical rated output braking torque, i.e., ΔTq0 = Tq3)... 5%) If the retarder braking torque is found to be abnormal, compensation will be initiated to avoid frequent corrections.

[0051] S5. Calculation and output of control current value for the proportional compensation valve: The retarder control unit calculates the proportional valve current compensation amount ΔI=I1 (△Tq / Tq3) Ka, where Ka is the compensation gain, is used to compensate for the retarder's response speed. If the braking torque deviation is positive, the normal compensation value of Ka is typically in the range of 1.0~1.1; if the braking torque deviation is negative, the normal compensation value of Ka is typically in the range of 1.0~0.9. The actual output current of the retarder control unit is I2 = I1 + ΔI. I2 is output through the PWM duty cycle to drive the proportional valve to adjust its opening. The main control valve responds synchronously, adjusting the fluid flow rate and oil pressure to compensate for the torque deviation.

[0052] S6, Cyclic Correction: The retarder control unit continuously collects signals and dynamically updates ΔTq and ΔI to achieve real-time compensation under all operating conditions, maintaining Tq1 close to Tq3 with a control accuracy of less than ±5%, and |ΔTq|≤ΔTq0 (ΔTq0=Tq3). 5%.

[0053] S7, Oil Temperature Viscosity Correction Factor Table (Self-Study): S7.1 self-learning trigger conditions: Within the same oil temperature range (e.g., 80℃~100℃) and the same speed range (e.g., 800rpm~1000rpm), the effective operating conditions that satisfy (speed n / actual oil pressure P1) within a set time t0 are less than or equal to ±5%, the retarder is fault-free, and the driver requests braking torque Tqd for ≥5s for a cumulative total of ≥m times (e.g., m is 10), and the retarder control unit calculates the actual average torque deviation ΔTq_avg based on the cumulative data for this range, satisfying that the actual average torque deviation |ΔTq_avg|>Tq3 within this oil temperature and speed range. 5%. If the above conditions are met, the self-learning condition will be triggered.

[0054] S7.2, Calculation of KL_k correction coefficient: The retarder control unit obtains the actual average torque deviation ΔTq_avg in this range, and combines it with the ratio of rated oil pressure P2 to actual oil pressure P1 to obtain the correction amount: Δk = (ΔTq_avg / Tq3). (P2 / P1) Kb, where Kb is the self-learning coefficient, is generally taken as 0.95 (range 0.8~1.0) to avoid over-correction and fluctuations.

[0055] S7.3, KL_k Correction Coefficient Table Update: Only update the k value for the current oil temperature range, while keeping other oil temperature ranges unchanged. k_new=k_old(1+△k), and store the updated coefficients in the corresponding temperature range. The corresponding coefficients will be directly called when the retarder works in the future.

[0056] S7.4 Special Restriction Handling: After changing the retarder fluid, the KL_k gauge can be manually zeroed using a diagnostic tool to restore it to its initial value and restart self-learning. Self-learning is limited by the fact that each KL_k value update must not exceed ±5%. If Δk is greater than 5%, the original value will be maintained to avoid excessive coefficient changes that could cause fluctuations in braking torque.

[0057] S8, Over-temperature torque limit: If the retarder is operating in over-temperature and over-torque limiting mode, this control method is disabled.

[0058] Through this embodiment: 1. No manual recalibration is required; it automatically adapts to the viscosity characteristics of aged oil, reducing maintenance costs. 2. Covers the entire temperature range and service life, compatible with both new and aged oils, and suitable for extreme high and low temperature conditions; 3. It can be achieved solely through software upgrades without the need for additional hardware.

[0059] Figure 3 This is a block diagram of an electronic device for a self-learning compensation control method for braking torque of a hydraulic retarder provided in one or more embodiments of the present invention.

[0060] Example 5, as Figure 3 As shown, the present invention provides an electronic device, 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; The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of a self-learning compensation control method for the braking torque of a hydraulic retarder.

[0061] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0062] The electronic device comprises a hardware layer, an operating system layer running on top of the hardware layer, and an application layer running on the operating system. The hardware layer includes hardware such as a central processing unit (CPU), a memory management unit (MMU), and memory. The operating system can be any one or more computer operating systems that control the electronic device through processes, such as Linux, Unix, Android, iOS, or Windows. Furthermore, in this embodiment of the invention, the electronic device can be a smartphone, tablet computer, or other handheld device, or a desktop computer, portable computer, or other electronic device; there is no particular limitation in this embodiment.

[0063] In this embodiment of the invention, the executing entity for electronic device control can be an electronic device itself, or a functional module within an electronic device capable of calling and executing a program. The electronic device can obtain the firmware corresponding to the storage medium. This firmware is provided by the supplier, and different storage media may have the same or different firmware; no limitation is made here. After obtaining the firmware corresponding to the storage medium, the electronic device can write this firmware into the storage medium; specifically, it burns the firmware corresponding to the storage medium into the storage medium. The process of burning the firmware into the storage medium can be implemented using existing technology, and will not be elaborated upon in this embodiment of the invention.

[0064] Electronic devices can also obtain reset commands corresponding to storage media. These reset commands are provided by the supplier, and the reset commands for different storage media can be the same or different, which is not limited here.

[0065] At this time, the storage medium of the electronic device is a storage medium on which the corresponding firmware has been written. The electronic device can respond to the reset command corresponding to the storage medium on which the corresponding firmware has been written, thereby resetting the storage medium on which the corresponding firmware has been written according to the reset command. The process of resetting the storage medium according to the reset command can be implemented by existing technology and will not be described in detail in this embodiment of the invention.

[0066] For ease of description, the above apparatus is described by dividing it into various units and modules according to their functions. Of course, in implementing this invention, the functions of each unit and module can be implemented in one or more software and / or hardware.

[0067] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the meaning consistent with their meaning in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined.

[0068] For the sake of simplicity, the method embodiments are described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0069] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A self-learning compensation control method for braking torque of a hydraulic retarder, characterized in that, include: Real-time acquisition of requested braking torque, actual braking torque, and fluid temperature; In response to the requested braking torque, the rated output braking torque is dynamically corrected based on the current oil temperature and the pre-stored viscosity correction coefficient, so as to obtain the target braking torque that is adapted to the current oil viscosity. Real-time comparison between actual braking torque and target braking torque; calculate torque deviation. Torque compensation is activated when the absolute value of the torque deviation exceeds the preset threshold. Calculate the compensation torque deviation based on the torque deviation; The system continuously collects signals and dynamically updates the deviation and compensation amount, keeping the deviation between the actual braking torque and the target braking torque within a preset accuracy range.

2. The self-learning compensation control method for braking torque of a hydraulic retarder according to claim 1, characterized in that, It also includes the step of self-learning the viscosity correction factor: Within the same oil temperature range and the same speed range, when the oil pressure fluctuation is within the set range, the retarder is fault-free, the effective working condition of continuously stable braking torque is accumulated to a preset number of times, and the average torque deviation of the range exceeds the threshold, self-learning is triggered. The correction amount of the viscosity correction coefficient is calculated based on the interval average torque deviation, the ratio of rated oil pressure to actual oil pressure, and the self-learning coefficient. Only the viscosity correction factor for the current oil temperature range is updated, while the viscosity correction factor for other temperature ranges remains unchanged; the magnitude of each update is limited to a preset threshold range, and the updated viscosity correction factor is stored.

3. The self-learning compensation control method for braking torque of a hydraulic retarder according to claim 2, characterized in that, include: The target braking torque Tq3 satisfies: Tq3 = Tq2 × k; Where Tq2 is the rated output braking torque under the current operating conditions, and k is the viscosity correction coefficient corresponding to the current oil temperature.

4. The self-learning compensation control method for braking torque of a hydraulic retarder according to claim 3, characterized in that, include: The proportional valve current compensation amount ΔI satisfies: ΔI=I1×(ΔTq / Tq3)×Ka; Where I1 is the current proportional valve base control current, ΔTq is the torque deviation, Tq3 is the target braking torque, and Ka is the compensation gain.

5. A self-learning compensation control system for braking torque of a hydraulic retarder, characterized in that, For implementing the self-learning compensation control method for braking torque of a hydraulic retarder as described in claim 1, the self-learning compensation control system for braking torque of the hydraulic retarder includes: The signal detection module includes an oil temperature sensor, a speed sensor, an oil pressure sensor, and a torque calculation unit, which are used to collect the oil temperature, rotor speed, and working oil pressure of the retarder, and output the calculated actual braking torque; and to acquire the requested braking torque in real time. The hydraulic control valve group, including the on / off valve, proportional valve, and main control valve, is connected to the oil circuit of the retarder working chamber and is used to adjust the oil pressure in the working chamber to change the output braking torque. The storage module is used to pre-store calibration parameters, including an oil temperature and viscosity correction coefficient table; The retarder control unit is connected to the signal detection module, the hydraulic control valve group, and the storage module respectively. The retarder control unit is used to respond to the requested braking torque, and dynamically correct the rated output braking torque according to the current oil temperature by calling the pre-stored viscosity correction coefficient to obtain the target braking torque that is adapted to the current oil viscosity. Then, based on the real-time comparison of the actual braking torque and the target braking torque, the torque deviation is calculated, the drive parameters of the hydraulic control valve group are adjusted in a closed loop, the viscosity correction coefficient of the current oil temperature range is updated, and the updated viscosity correction coefficient is stored.

6. The hydraulic retarder braking torque self-learning compensation control system according to claim 5, characterized in that, include: The switching valve, proportional valve, and main control valve are located between the main oil supply circuit of the retarder and the working chamber of the retarder. The proportional valve is used to continuously adjust the flow rate and oil pressure of the main oil circuit supplying the retarder through the PWM duty cycle. The main control valve is mechanically passively linked with the oil pressure in the working chamber. The switching valve is used to open and close the main oil circuit supplying the retarder.

7. The hydraulic retarder braking torque self-learning compensation control system according to claim 5, characterized in that, include: The retarder control unit includes a feedforward correction unit, a closed-loop compensation unit, and a self-learning iteration unit. The feedforward correction unit is used to call the viscosity correction coefficient according to the oil temperature to calculate the target braking torque that is adapted to the current viscosity. The closed-loop compensation unit is used to calculate the torque deviation based on the target braking torque and actual braking torque of the current year calculated by the feedforward correction unit, output the proportional valve current compensation amount, and drive the hydraulic control valve group to adjust the oil pressure. The self-learning iterative unit is used to accumulate data under steady-state effective operating conditions, calculate the average torque deviation based on the torque deviation calculated by the closed-loop compensation unit, and update the viscosity correction coefficient by temperature zone.

8. The self-learning compensation control system for braking torque of a hydraulic retarder according to claim 5, characterized in that, include: The self-learning iterative unit is configured with a limiting protection subunit to limit the update amplitude of the viscosity correction coefficient in a single operation to no more than a preset threshold range. The self-learning iteration unit is also equipped with a reset interface, which supports clearing the viscosity correction coefficient table and restoring the factory initial value after oil replacement via a diagnostic instrument.

9. The hydraulic retarder braking torque self-learning compensation control system according to claim 5, characterized in that, include: The retarder control unit is equipped with an over-temperature shielding unit, which is used to shield the torque compensation and self-learning iteration process when the retarder enters the over-temperature torque limiting mode.

10. An electronic device, characterized in that, include: The processor, communication interface, memory, and communication bus are connected, with the processor, communication interface, and memory communicating with each other via the communication bus. The memory stores a computer program that, when executed by a processor, causes the processor to perform the steps of the hydraulic retarder braking torque self-learning compensation control method as described in any one of claims 1 to 4.

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