Integrated electric drive oil life prediction method and system

Abstract

The invention relates to an integrated electric drive oil service life prediction method which comprises the following steps: acquiring first data of an oil sample reaching the oil change service life, and constructing a composite service life model based on the first data; the first data comprises the viscosity of the oil liquid sample, the residual content of the antioxidant and the granularity; based on the composite life model, taking the environment temperature and the storage duration of the oil sample as first damage factors, and constructing a first function based on the environment temperature and the storage duration; the motor torque and the motor rotating speed borne by the oil sample in the using process serve as second damage factors. According to the integrated electric drive oil service life prediction method and system, the optimal oil change mileage based on the actual driving working condition and the temperature load can be provided for each trolley, the oil change period can be remarkably prolonged for a user under the light use working condition, and the maintenance cost can be reduced; and more accurate oil service life prediction and oil change guidance can be provided for users driving intensely, so that the system performance and safety are ensured.

Description

Technical Field

[0001] This invention relates to the field of electric drive systems for new energy vehicles, and specifically to an integrated electric drive fluid life prediction method and system. Background Technology

[0002] With the development of electrification in the automotive industry, more and more models are switching their powertrains from traditional fuel-powered to electric-powered systems. Among them, integrated electric drive systems have become the main application direction due to their advantages such as miniaturization, lightweighting, and integration. However, the oil life of integrated electric drive systems is a core indicator affecting the system's reliability, efficiency, and long-term performance. In integrated electric drive systems, the reducer gear lubrication and motor stator and rotor cooling share the same oil, and its life is affected by multiple factors such as oil characteristics, working environment, load intensity, and maintenance methods. Although pure electric dedicated fully synthetic electric drive cooling oil can effectively resist thermal oxidation, reduce oil decomposition and carbon deposits, and ensure good lubrication performance in extreme working environments, in actual applications, the oil life will still gradually decrease due to various factors.

[0003] Currently, the assessment of integrated electric drive fluid life mainly relies on empirical data, lacking correlation with vehicle parameters and electric drive assembly parameters. Furthermore, existing fluid life prediction methods are mostly based on single operating conditions, making it difficult to accurately reflect fluid life changes during actual use. This makes it difficult to accurately predict fluid life under different operating conditions, thus affecting system reliability and maintenance efficiency. Summary of the Invention

[0004] Therefore, this application provides an integrated electric drive oil life prediction method and system to solve the above problems.

[0005] In a first aspect, embodiments of this application provide an integrated electric drive oil life prediction method, comprising the following steps: First data of oil samples that have reached their oil change life is obtained, and a composite life model is constructed based on the first data; the first data includes the viscosity, residual antioxidant content and particle size of the oil sample; Based on the composite life model, the ambient temperature and storage time of the oil sample are used as the first damage factor to construct a first function based on ambient temperature and storage time; the motor torque and motor speed experienced by the oil sample during use are used as the second damage factor to construct a second function based on motor torque and motor speed. A lifetime prediction model is constructed by combining the first and second functions, and the lifetime prediction model is calibrated through durability tests. The calibrated life prediction model is embedded into the vehicle control unit. Based on the ambient temperature data, storage time data, motor speed data and motor torque data received in real time by the vehicle control unit, the remaining life data of the integrated electric drive fluid is calculated and output.

[0006] In conjunction with the first aspect, in one implementation, the expression for the composite lifetime model is: ; Where RULER represents the residual antioxidant content of the oil sample, and Granularity represents the oil particle size of the oil sample. This represents the viscosity of the oil sample.

[0007] In conjunction with the first aspect, in one implementation, the expression of the first function is: ; Where T is the storage duration. The ambient temperature.

[0008] In conjunction with the first aspect, in one implementation, the expression for the second function is: ; in, for The corresponding motor speed at any given time for The motor torque at time t is the motor speed at which the oil life is affected, n is the motor speed at which the oil life is affected, m is the motor torque at which the oil life is affected, and t is the cumulative running time.

[0009] In conjunction with the first aspect, in one implementation, the expression for the lifetime prediction model is: ; Where T is the storage duration. For ambient temperature, for The corresponding motor speed at any given time for The motor torque at time t is the motor speed at which the oil life is affected, n is the motor speed at which the oil life is affected, m is the motor torque at which the oil life is affected, and t is the cumulative running time.

[0010] In conjunction with the first aspect, in one embodiment, the method for obtaining the damage contribution index of motor torque to oil life and the damage contribution index of motor speed to oil life includes: Based on the remaining life output by the composite life model, multiple integrated electric drive assemblies in the same state were run under different combinations of motor torque and motor speed in bench durability tests. At a set temperature, oil was extracted and its viscosity, residual antioxidant content and particle size were tested at multiple set time points under various working conditions to obtain the measured value of remaining life. The measured remaining lifespan values ​​are substituted into the lifespan prediction model, and the motor speed damage contribution index n and the motor torque damage contribution index m are solved by fitting using the maximum likelihood method until the fitting error is less than the preset threshold.

[0011] In conjunction with the first aspect, in one implementation, after embedding the calibrated life prediction model into the vehicle control unit, and calculating and outputting the remaining life data of the integrated electric drive fluid based on the ambient temperature data, storage duration data, motor speed data, and motor torque data received in real time by the vehicle control unit, the method further includes: The decision to replace the fluid is based on the remaining lifespan data and the lifespan threshold.

[0012] In conjunction with the first aspect, in one implementation, determining whether the oil needs to be replaced based on the remaining lifespan data and the lifespan threshold specifically includes: When the remaining life data is greater than or equal to the life threshold, it is determined that the oil does not need to be replaced; When the remaining lifespan data is less than the lifespan threshold, it is determined that the oil needs to be replaced.

[0013] In conjunction with the first aspect, in one embodiment, the lifespan threshold is determined by the remaining lifespan quantile calculated by a composite lifespan model for oil samples that have reached their oil change lifespan.

[0014] Secondly, embodiments of this application provide an integrated electric drive oil life prediction system, comprising: Data acquisition module: used to acquire the first data of oil samples that have reached the end of their oil change life; The model building module is used to build a composite life model based on the data from the data acquisition module. It can also build a first function based on ambient temperature and storage time, a second function based on motor torque and motor speed, and combine the first and second functions to build a life prediction model. The life prediction model is then calibrated through durability tests. The vehicle control module is used to receive ambient temperature data, storage time data, motor speed data and motor torque data obtained by the vehicle control unit in real time, and execute the calibrated life prediction model to calculate and output the remaining life data of the integrated electric drive fluid. Interaction module: Used to import the calibrated life prediction model into the vehicle control module.

[0015] The beneficial effects of the technical solutions provided in this application include: This integrated electric drive fluid life prediction method and system innovatively constructs a life prediction model by acquiring real-time data on ambient temperature, storage time, motor speed, and motor torque during vehicle operation. This model is then embedded into the vehicle's software. Based on the acquired real-time data, it can provide each vehicle with the optimal oil change interval based on actual driving conditions and temperature load. For users with light driving conditions, it can significantly extend the oil change cycle and reduce maintenance costs. For users with aggressive driving conditions, it can provide more accurate fluid life prediction and oil change guidance, ensuring system performance and safety.

[0016] This integrated electric drive fluid life prediction method and system, by embedding the life prediction model into the vehicle control unit, can monitor the vehicle's operating status in real time and predict the fluid life in real time, ensuring the accuracy and timeliness of the prediction results.

[0017] This integrated electric drive oil life prediction method and system not only improves oil maintenance efficiency, but also reduces unnecessary oil replacements through accurate prediction, optimizes resource utilization, and lowers operating costs. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the main process of the present invention; Figure 2 This is a schematic diagram of the process of the present invention; Figure 3 This is a schematic diagram illustrating the process of reminding you to change the oil in this invention. Detailed Implementation

[0020] 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 some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0021] Please see Figure 1 This application provides an integrated electric drive oil life prediction method, including the following steps: S1. Obtain the first data of the oil sample that has reached the oil change life, and construct a composite life model based on the first data; S2. Based on the composite life model, the ambient temperature and storage time of the oil sample are used as the first damage factor to construct a first function based on the ambient temperature and storage time; the motor torque and motor speed experienced by the oil sample during use are used as the second damage factor to construct a second function based on the motor torque and motor speed. S3. Construct a lifetime prediction model by combining the first and second functions, and calibrate the lifetime prediction model through durability tests. S4. Embed the calibrated life prediction model into the vehicle control unit. Based on the ambient temperature data, storage time data, motor speed data and motor torque data received in real time by the vehicle control unit, calculate and output the remaining life data of the integrated electric drive fluid.

[0022] Through the above steps, an innovative life prediction model was constructed based on ambient temperature, storage time, motor speed, and motor torque data as factors affecting oil life. This model was then embedded into the vehicle software, predicting oil life in real time based on the acquired real-time data. This ensures the accuracy and timeliness of the prediction results, thereby providing more accurate oil life prediction and oil change guidance, ensuring system performance and safety.

[0023] To make the above steps easier to understand, specifically, as follows: Figure 2 As shown: Step 1: Obtain the first data of the oil sample that has reached the end of its oil change life, and construct a composite life model based on the first data; the first data includes the viscosity, remaining antioxidant content and particle size of the oil sample; The viscosity of the oil sample was obtained using the rotational viscosity method, the residual antioxidant content of the oil sample was obtained using the linear voltammetry method, and the particle size of the oil sample was obtained using the resistance method.

[0024] Specifically, 100 oil samples from vehicles that have reached their oil change life cycle (preferably 80,000 km in this embodiment) are extracted and analyzed. Viscosity, residual antioxidant content, and particle size are analyzed as three quantitative indicators. The top 20% quantiles of these three indicators are taken as the life threshold corresponding to the oil change cycle, and compared with the indicators of oil samples from the initial 0 km mark. The contribution ratio of the three quantitative indicators to the deterioration of oil life is fitted, and the threshold conditions for the three quantitative indicators are obtained as follows: Viscosity increased by more than 20%; The remaining antioxidant content is 30% of the original content; Particle size > 10 μm and particle number > 1000 particles / mL; Establish a composite lifetime model: ; Where RULER represents the residual antioxidant content of the oil sample, and Granularity represents the oil particle size of the oil sample. The viscosity of the oil sample; When the above threshold conditions are met simultaneously, the composite lifetime model outputs a remaining lifetime of 10%, that is, a lifetime threshold of 10%.

[0025] Step 2: Based on the composite lifetime model, the ambient temperature and storage time of the oil sample are used as the first damage factor to construct a first function based on ambient temperature and storage time; This embodiment analyzes three quantitative indicators—viscosity, residual antioxidant content, and particle size—based on oil samples stored at different temperatures and for different times. The experimental design is shown in the table below:

[0026] Based on the above data, the first function is fitted: ; Where T is the storage duration. The ambient temperature is represented by 'e', ​​and 'e' is a number (e.g., 2.71828...).

[0027] The first function can quantify the combined effects of ambient temperature and storage time on oil life.

[0028] Step 3: Based on the composite life model, the motor torque and motor speed experienced by the oil sample during use are used as the second damage factors to construct a second function based on motor torque and motor speed; Among them, based on the mechanical shear effect: the higher the motor speed, the greater the shear force on the oil and the faster the lifespan is reduced; and the load effect: the greater the motor torque, the greater the mechanical load on the oil and the faster the lifespan is reduced. Fitting the second function: ; in, for The corresponding motor speed at any given time for The motor torque at time t corresponds to the motor torque at time t, where n is the damage contribution index of motor speed to oil life, m is the damage contribution index of motor torque to oil life, and t is the cumulative running time. It represents a tiny change in time and is used for integration operations.

[0029] Step 4: Construct a lifetime prediction model by combining the first and second functions, and calibrate the lifetime prediction model through durability tests; The combined lifetime prediction model is expressed as follows: ; Where T is the storage duration. For ambient temperature, for The corresponding motor speed at any given time for The motor torque at time t corresponds to the motor torque at time t, where n is the damage contribution index of motor speed to oil life, m is the damage contribution index of motor torque to oil life, and t is the cumulative running time. It represents a tiny change in time and is used for integration operations.

[0030] The methods for obtaining the damage contribution index of motor torque to oil life and the damage contribution index of motor speed to oil life include: Based on the remaining life output by the composite life model, multiple integrated electric drive assemblies in the same state were run under different combinations of motor torque and motor speed in bench durability tests. At a set temperature, oil was extracted and its viscosity, residual antioxidant content and particle size were tested at multiple set time points under various working conditions to obtain the measured value of remaining life. The measured remaining lifespan values ​​are substituted into the lifespan prediction model, and the motor speed damage contribution index n and the motor torque damage contribution index m are solved by fitting using the maximum likelihood method until the fitting error is less than the preset threshold.

[0031] Specifically: Motor torque and motor speed are classified according to different proportions, and the working condition matrix is ​​designed as shown in the table below.

[0032]

[0033] Among them, operating condition sequences 1-9 correspond to different amounts of motor torque and motor speed. Operating condition 1 corresponds to the largest torque and speed, and also the greatest mechanical damage. Operating condition combination sequence 9 corresponds to the smallest torque and speed, and also the least mechanical damage. Nine electric drive assemblies in the same condition were selected and filled with the same amount of oil. Durability tests were conducted according to operating conditions 1-9. All durability tests were carried out at room temperature. The oil at different storage time points for each test unit was tested and analyzed to obtain three quantitative indicators: viscosity, residual antioxidant content, and particle size. The actual lifespan value corresponding to that time period was calculated. The obtained data is shown in the table below:

[0034] By solving for the values ​​of N and M using the maximum likelihood method, we finally obtain: Solving for m, we get m = 3.4 and n = 2.1. Right now: .

[0035] Step 5: Embed the calibrated life prediction model into the vehicle control unit. Based on the ambient temperature data, storage time data, motor speed data and motor torque data received in real time by the vehicle control unit, calculate and output the remaining life data of the integrated electric drive fluid.

[0036] Specifically: The mathematical expression of the life prediction model is converted into programming language code for execution on the microprocessor of the vehicle control unit. In the software system of the vehicle control unit, a data interface is established to receive real-time data, including ambient temperature data, storage time data, motor speed data, and motor torque data. These data are transmitted to the vehicle control unit through the vehicle's communication network. The code of the life prediction model is embedded in the main program of the vehicle control unit, and the life prediction model is called periodically in the loop of the main program. The life prediction model calculates the remaining life of the fluid based on the input real-time data and stores the result in the memory of the vehicle control unit. The calculated remaining life data of the fluid is output to the vehicle's instrument panel or central control screen through the communication interface of the vehicle control unit for the driver to view.

[0037] In addition, it also includes: Step Six: Determine whether the fluid needs to be replaced based on the remaining lifespan data and the lifespan threshold. The 10% lifespan threshold obtained in step one can be used to determine whether the fluid needs to be replaced. Specifically, such as Figure 3 As shown, when the remaining lifespan is greater than or equal to 10%, it is determined that the oil does not need to be replaced. When the remaining lifespan is less than 10%, it is determined that the fluid needs to be replaced.

[0038] In addition, embodiments of this application provide an integrated electric drive oil life prediction system, including: Data acquisition module: used to acquire the first data of oil samples that have reached the end of their oil change life. The first data refers to the viscosity, residual antioxidant content and particle size of the oil sample. Based on the above data, parameters that conform to the life model can be obtained. The model building module is used to build a composite life model based on the data from the data acquisition module. It can also build a first function based on ambient temperature and storage time, a second function based on motor torque and motor speed, and combine the first and second functions to build a life prediction model. The life prediction model is then calibrated through durability tests. Interaction module: It is used to import the calibrated life prediction model into the vehicle control unit to realize information interaction with the vehicle. The ambient temperature data, storage time data, motor speed data and motor torque data obtained by the vehicle control unit will be input into the imported life prediction model to output the remaining life data of the integrated electric drive fluid.

[0039] The combined effect of the above modules enables accurate prediction of the lifespan of the integrated electric drive fluid, thus providing each vehicle with the optimal oil change interval based on actual driving conditions and temperature load. For users with light usage conditions, this can significantly extend the oil change cycle and reduce maintenance costs; while for users with aggressive driving conditions, it can provide more accurate fluid life prediction and oil change guidance, ensuring system performance and safety.

[0040] In the description of this application, it should be noted that the terms "upper," "lower," etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Unless otherwise expressly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication between two elements. For those skilled in the art, the specific meaning of the above terms in this application can be understood according to the specific circumstances.

[0041] It should be noted that in this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0042] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for predicting the lifespan of an integrated electric drive fluid, characterized in that, Includes the following steps: First data of oil samples that have reached their oil change life is obtained, and a composite life model is constructed based on the first data; the first data includes the viscosity, residual antioxidant content and particle size of the oil sample; Based on the composite life model, the ambient temperature and storage time of the oil sample are used as the first damage factor to construct a first function based on ambient temperature and storage time; the motor torque and motor speed experienced by the oil sample during use are used as the second damage factor to construct a second function based on motor torque and motor speed. A lifetime prediction model is constructed by combining the first and second functions, and the lifetime prediction model is calibrated through durability tests. The calibrated life prediction model is embedded into the vehicle control unit. Based on the ambient temperature data, storage time data, motor speed data and motor torque data received in real time by the vehicle control unit, the remaining life data of the integrated electric drive fluid is calculated and output.

2. The integrated electric drive oil life prediction method according to claim 1, characterized in that, The expression for the composite lifetime model is: ; Where RULER represents the residual antioxidant content of the oil sample, and Granularity represents the oil particle size of the oil sample. This represents the viscosity of the oil sample.

3. The integrated electric drive oil life prediction method according to claim 1, characterized in that, The expression for the first function is: ； Where T is the storage duration. The ambient temperature.

4. The integrated electric drive oil life prediction method according to claim 1, characterized in that, The expression for the second function is: ； in, for The corresponding motor speed at any given time for The motor torque at time t is the motor speed at which the oil life is affected, n is the motor speed at which the oil life is affected, m is the motor torque at which the oil life is affected, and t is the cumulative running time.

5. The integrated electric drive oil life prediction method according to claim 1, characterized in that, The expression for the lifetime prediction model is: ; Where T is the storage duration. For ambient temperature, for The corresponding motor speed at any given time for The motor torque at time t is the motor speed at which the oil life is affected, n is the motor speed at which the oil life is affected, m is the motor torque at which the oil life is affected, and t is the cumulative running time.

6. The integrated electric drive oil life prediction method according to claim 5, characterized in that, The methods for obtaining the damage contribution index of motor torque to oil life and the damage contribution index of motor speed to oil life include: Based on the remaining life output by the composite life model, multiple integrated electric drive assemblies in the same state were run under different combinations of motor torque and motor speed in bench durability tests. At a set temperature, oil was extracted and its viscosity, residual antioxidant content and particle size were tested at multiple set time points under various working conditions to obtain the measured value of remaining life. The measured remaining lifespan values ​​are substituted into the lifespan prediction model, and the motor speed damage contribution index n and the motor torque damage contribution index m are solved by fitting using the maximum likelihood method until the fitting error is less than the preset threshold.

7. The integrated electric drive oil life prediction method according to claim 1, characterized in that, The process of embedding the calibrated life prediction model into the vehicle control unit, and calculating and outputting the remaining life data of the integrated electric drive fluid based on the ambient temperature data, storage time data, motor speed data, and motor torque data received in real time by the vehicle control unit, further includes: The decision to replace the fluid is based on the remaining lifespan data and the lifespan threshold.

8. The integrated electric drive oil life prediction method according to claim 7, characterized in that, The process of determining whether to replace the fluid based on the remaining lifespan data and the lifespan threshold specifically includes: When the remaining life data is greater than or equal to the life threshold, it is determined that the oil does not need to be replaced; When the remaining lifespan data is less than the lifespan threshold, it is determined that the oil needs to be replaced.

9. The integrated electric drive oil life prediction method according to claim 8, characterized in that, The lifespan threshold is determined by the remaining lifespan quantile value calculated by the composite lifespan model for oil samples that have reached their oil change lifespan.

10. An integrated electric drive oil life prediction system, characterized in that, include: Data acquisition module: used to acquire the first data of oil samples that have reached the end of their oil change life; The model building module is used to build a composite life model based on the data from the data acquisition module. It can also build a first function based on ambient temperature and storage time, a second function based on motor torque and motor speed, and combine the first and second functions to build a life prediction model. The life prediction model is then calibrated through durability tests. Interaction module: Used to import the calibrated life prediction model into the vehicle control unit.

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