A method for predicting the gear life of the main reducer of a trolleybus based on the CLTC-P operating condition.

Abstract

This invention relates to the field of gear life prediction technology, and discloses a method for predicting the gear life of a trolley main reducer based on the CLTC-P operating condition. The method includes: obtaining the vehicle's speed-time series and related parameters, and combining this with the vehicle's driving resistance balance equation to calculate the input torque history of the main reducer. Next, based on the gear contact strength verification method, the variation history of gear contact stress is analyzed and determined. The contact stress is statistically analyzed using the growth curve method to obtain the cyclic frequency distribution corresponding to the stress amplitude. Subsequently, the Goodman linear life correction method is used to transform asymmetric stress into symmetric cyclic stress, establishing a stress amplitude frequency spectrum. Combining the material coefficients and S-N curves of 20CrMnTi steel, the linear fatigue cumulative damage method is used to accumulate fatigue damage on the gear, ultimately predicting the gear's fatigue life.

Description

Technical Field

[0001] This invention relates to the field of gear life prediction technology, and more specifically, to a method for predicting the life of trolley main reducer gears based on the CLTC-P operating condition. Background Technology

[0002] With the rapid development of electric vehicles, the trolley main reducer, as a key component of the drive system, is subjected to constantly changing loads and stresses. In the design and use of trolley main reducers, gear fatigue life prediction has become one of the core technologies to ensure their reliability and safety.

[0003] Currently, traditional gear life prediction methods are typically based on simplified experimental data or static condition analysis, failing to fully consider the dynamic changes in operating conditions experienced by trolleybuses during actual operation. This leads to significant discrepancies between predicted results and actual conditions. Therefore, accurately predicting gear life based on actual vehicle operating conditions has become a crucial issue in current research and engineering applications. Furthermore, the fatigue life of trolleybus main reducer gears is influenced by multiple factors, including vehicle operating conditions, gear contact stress, material properties, and load variations. Traditional fatigue life prediction methods often rely on simplified assumptions, such as constant load and linear material behavior, which cannot accurately reflect the dynamic changes of trolleybuses under different operating conditions. In addition, existing prediction methods typically lack comprehensive consideration of factors such as different speeds, accelerations, and resistance, making them ill-suited to the diversity of actual road conditions and driving behaviors. Therefore, improving the accuracy of fatigue life prediction methods is an urgent problem to be solved.

[0004] Therefore, there is an urgent need to invent a prediction technology for the life of the main reducer gears of trolley vehicles, in order to solve the problem that the existing technology cannot accurately reflect the dynamic operating conditions of trolley vehicles during actual operation, resulting in poor accuracy in predicting the life of the main reducer gears. Summary of the Invention

[0005] In view of this, the present invention proposes a method for predicting the life of the main reducer gears of a trolley based on the CLTC-P operating condition, aiming to solve the problem that the current technology cannot accurately reflect the dynamic operating condition changes of the trolley during actual operation, resulting in poor accuracy in predicting the life of the main reducer gears.

[0006] This invention proposes a method for predicting the gear life of a trolley main reducer based on the CLTC-P operating condition, comprising:

[0007] Obtain the vehicle parameters of CLTC-P and the speed-time series of CLTC-P under various road conditions;

[0008] Based on the vehicle driving resistance balance equation, vehicle parameters and speed-time series, the torque history of the main reducer input during the preset time period is determined, and the change history of the contact stress between the first-stage gear and the second-stage gear of the main reducer during the preset time period is determined according to the torque history during the preset time period and the gear contact strength verification method.

[0009] Based on the growth curve method, load spectrum statistics were performed on the time change history of contact stress to obtain the cycle frequency distribution corresponding to each stress amplitude between the first-stage gear and the second-stage gear.

[0010] Based on the Goodman linear life correction method, the stress with non-zero average value in the cycle frequency corresponding to each stress amplitude is transformed into equivalent symmetrical cyclic stress. Based on the cycle frequency distribution corresponding to each stress amplitude and the equivalent symmetrical cyclic stress, a stress amplitude frequency spectrum is established.

[0011] Based on the effective stress concentration factor, size factor, surface finish factor, and dispersion factor of 20CrMnTi steel, the SN curve corresponding to the gear of the main reducer is obtained;

[0012] Based on the linear fatigue cumulative damage theory, stress amplitude frequency spectrum and SN curve, the fatigue life prediction result of the main reducer gear is determined.

[0013] Furthermore, when obtaining the vehicle parameters of CLTC-P and the speed-time series of CLTC-P under various road conditions, the following are included:

[0014] The CLTC-P standard velocity-time curve is discretely sampled based on a preset sampling time interval to obtain a set of discrete velocity-time points; the discrete sampling points are initially smoothed based on a dynamic noise adaptive filtering algorithm to reduce the influence of measurement noise and abrupt changes.

[0015] A high-precision continuous velocity-time curve is generated by interpolating a discrete set of velocity-time points using a multi-resolution linear interpolation method.

[0016] The interpolated velocity-time curve is denoised, and an adaptive threshold filtering method is introduced to balance noise suppression and signal fidelity, while the time step of the denoised velocity-time curve is unified.

[0017] Based on the CLTC-P working condition definition and intelligent clustering algorithm, the processed speed-time curve is segmented into low-speed, medium-speed, high-speed and idle speed ranges. At the same time, abnormal acceleration and deceleration sections are automatically identified, and speed-time series under each road condition are established.

[0018] The machine learning-based operating condition prediction module predicts future operating condition trends based on historical speed-time series characteristics and dynamically corrects the generated speed-time series to enhance the series' adaptability to actual driving behavior and prediction accuracy.

[0019] Furthermore, based on the vehicle's driving resistance balance equation, vehicle parameters, and speed-time series, the torque history of the main reducer input during a preset time period is determined, including:

[0020] Based on vehicle dynamics, a vehicle driving resistance balance equation is established, and the rolling resistance coefficient and air resistance coefficient are dynamically corrected based on an adjustable resistance coefficient model.

[0021] Based on the speed-time series under various road conditions, the vehicle acceleration at each moment is obtained using an adaptive high-order numerical differentiation method, and the peak acceleration value is corrected by filtering.

[0022] Based on vehicle parameters, rolling resistance coefficient, air resistance coefficient, frontal area, wheel rolling radius and road slope angle, the total driving resistance at each moment is determined, and the total driving resistance value is corrected according to temperature, wind speed and road friction coefficient by the environmental factor compensation module.

[0023] Based on the total driving resistance, wheel rolling radius, main reduction ratio and transmission efficiency at each moment, the input torque at the input end of the main reducer at each moment is determined, and a dataset of torque changing with time is generated based on an adaptive smoothing algorithm.

[0024] Based on the preset time period, the input torque history for the corresponding time period is obtained from the torque dataset, and the future torque change trend is dynamically corrected based on the machine learning prediction module.

[0025] Furthermore, when determining the input torque at the input end of the main reducer at each moment, based on the total driving resistance, wheel rolling radius, final drive ratio, and transmission efficiency, the following steps are taken:

[0026] Obtain the rolling resistance, air resistance, gradient resistance, and acceleration resistance of the vehicle at each time point, and determine the total driving resistance of the vehicle at each time point based on the rolling resistance, air resistance, gradient resistance, and acceleration resistance of the vehicle at each time point.

[0027] Based on the total driving resistance at each moment and the rolling radius of the vehicle's wheels, the torque acting on the wheel side at each moment is obtained.

[0028] Based on the relationship between the torque acting on the wheel side at each moment and the main reduction ratio and transmission efficiency, and using formula (1), the input torque Tt at the input end of the main reducer at each moment is obtained:

[0029] (1)

[0030] in, Zt Let be the total driving resistance between different times, r be the wheel rolling radius, io be the principal reduction ratio, and η be the transmission efficiency.

[0031] Furthermore, based on the torque history over a preset time period and the gear contact strength verification method, when determining the variation history of the contact stress between the first-stage and second-stage gears of the main reducer within a preset time period, the following is included:

[0032] Based on the total driving resistance and gear geometry parameters at each moment, and based on formula (2), the dynamic circumferential force on the pitch circle is determined:

[0033] (2)

[0034] in, Ft The dynamic circumferential force on the pitch circle is d, where d is the pitch circle diameter of the pinion.

[0035] Based on the end face pressure angle and base cylinder helix angle between the first-stage and second-stage gears, and formula (3), the node region coefficient between the first-stage and second-stage gears is obtained:

[0036] (3)

[0037] in Z H This is the node region coefficient between the first-stage and second-stage gears. α t The end face pressure angle, β b Based on the helix angle of the base cylinder, The end face engagement angle;

[0038] Based on the elastic modulus of the first gear material, the elastic modulus of the second gear material, the Poisson's ratio of the first gear and the Poisson's ratio of the second gear, and formula (4), the material elastic coefficients between the first-stage gear and the second-stage gear are determined:

[0039] (4)

[0040] in, Z E The elastic modulus of the material between the first-stage gear and the second-stage gear. E 1 Let be the elastic modulus of the gear material of the first gear. E 2 The elastic modulus of the gear material for the second gear. v 1 Let the Poisson's ratio be the first gear. v 2 The Poisson's ratio of the second gear;

[0041] Based on the end face overlap and longitudinal overlap between the first-stage and second-stage gears, and formula (5), determine the overlap ratio and helix angle coefficient between the first-stage and second-stage gears:

[0042] (5)

[0043] in, Z εβ This refers to the overlap ratio and helix angle coefficient between the first-stage and second-stage gears. ε α The overlap ratio of the end faces between the first-stage gear and the second-stage gear. ε β This refers to the longitudinal overlap between the first-stage and second-stage gears. β The helix angle of the pinion;

[0044] The instantaneous contact stress between the first and second gears is determined based on the dynamic circumferential force on the pitch circle, the node region coefficient between the first and second gears, the material elastic coefficient, and the overlap and helix angle coefficients.

[0045] Based on the instantaneous contact stress between the primary and secondary gears of the main reducer within a preset time period, the variation history of the contact stress between the primary and secondary gears within the preset time period is generated.

[0046] Furthermore, when obtaining the pitch circle diameter of the pinion, the following steps are included:

[0047] Obtain the normal module, helix angle, and number of teeth of the pinion, and obtain the pitch circle diameter of the pinion based on the normal module, helix angle, and number of teeth of the first-stage gear and formula (6):

[0048] (6)

[0049] in, m n Let be the normal module of the pinion. β The helix angle of the pinion. z This refers to the number of teeth.

[0050] Furthermore, based on the dynamic circumferential force on the pitch circle, the node region coefficient between the first-stage and second-stage gears, the material elastic coefficient, the overlap ratio and helix angle coefficient, and formula (7), the instantaneous contact stress between the first-stage and second-stage gears is determined, including:

[0051] (7)

[0052] in, σH The instantaneous contact stress between the first-stage gear and the second-stage gear. bThe tooth widths of the first and second gears. K A For the use factor, K v This is the dynamic load factor. K Hβ The tooth load distribution factor is the tooth direction load distribution factor. K Hα This is the inter-tooth load distribution coefficient.

[0053] Furthermore, based on the growth curve method, load spectrum statistics are performed on the time variation history of contact stress to obtain the cycle frequency distribution corresponding to each stress amplitude between the first-stage and second-stage gears, including:

[0054] Extreme value analysis is performed on the contact stress-time series within a preset time period, and all stress peaks and valleys are identified to form a complete stress cycle unit;

[0055] Obtain the stress amplitude and average stress of each stress cycle unit, and classify the stress amplitude according to the preset stress level range to establish a 16-level stress amplitude level classification standard;

[0056] The number of cycles corresponding to each stress level is counted to generate the cycle frequency distribution corresponding to each stress amplitude between the first-stage gear and the second-stage gear.

[0057] Furthermore, when determining the predicted fatigue life of the main reducer gear based on the linear fatigue cumulative damage theory, stress amplitude frequency spectrum, and SN curve, the following are included:

[0058] Obtain the SN curve corresponding to the gear material of the main reducer. Based on the stress amplitude frequency spectrum and equivalent symmetrical cyclic stress, determine the fatigue life of the gear, where:

[0059] Based on the stress amplitudes and formula (8), determine the damage degree of the gear material in the main reducer:

[0060] (8)

[0061] in, D i Damage degree of the main reducer gear material, n i N represents the number of cycles at the i-th stress amplitude. i The lifetime is the lifespan at the i-th stress amplitude.

[0062] Based on the linear fatigue cumulative damage method, the damage degree of each stress amplitude is accumulated, and the total damage degree of the main reducer gear material is determined based on formula (9):

[0063] (9)

[0064] Among them, R is the total damage degree of the main reducer gear material;

[0065] Based on the relationship between the total damage degree of the main reducer gear material and the preset total damage degree, determine whether the main reducer gear has reached its fatigue life:

[0066] When the total damage degree of the main reducer gear material is higher than or equal to the preset total damage degree, the main reducer gear is determined to have reached its fatigue life.

[0067] When the total damage degree of the main reducer gear material is lower than the preset total damage degree, it is determined that the main reducer gear has not reached its fatigue life. The fatigue warning level of the main reducer gear is determined based on the damage degree difference between the total damage degree of the main reducer gear material and the preset total damage degree.

[0068] Furthermore, when determining the fatigue warning level of the main reducer gear based on the damage difference between the total damage degree of the main reducer gear material and the preset total damage degree, the following are included:

[0069] Based on the relationship between the damage difference and the configured first and second preset damage differences, the fatigue warning level of the main reducer gear is determined:

[0070] When the damage difference is lower than the first preset damage difference, the fatigue warning level of the main reducer gear is determined to be a high-risk level.

[0071] When the damage difference is higher than or equal to the first preset damage difference and lower than the second preset damage difference, the fatigue warning level of the main reducer gear is determined to be a medium risk level.

[0072] When the damage difference is higher than or equal to the second preset damage difference, the fatigue warning level of the main reducer gear is determined to be low risk level.

[0073] Among them, the first preset damage difference is less than the second preset damage difference.

[0074] Compared with existing technologies, the advantages of this invention are as follows: By introducing vehicle driving parameters and speed-time series under the CLTC-P condition, the dynamic operating conditions of the tram in actual operation are fully considered. This method can accurately simulate the operating state of the tram under different conditions, such as low-speed, medium-speed, high-speed, and idling ranges, solving the problem of neglecting actual road condition changes common in traditional methods. Through these dynamic data, the changes in gear contact stress during actual driving can be simulated more accurately, thereby effectively improving the accuracy of gear life prediction. In addition, this invention uses the growth curve method to perform load spectrum statistics on the time change history of contact stress and analyzes it based on the cycle number distribution of different stress amplitudes. This method can make more detailed predictions of gear fatigue life based on stress amplitude, avoiding the overly simplified stress distribution assumptions in traditional methods. By establishing a stress amplitude frequency spectrum and combining it with the Goodman linear life correction method, the asymmetric stress in actual operation can be transformed into symmetric stress, thereby further improving the accuracy of life prediction. In terms of materials, this invention uses the effective stress concentration factor, size factor, surface finish factor, and dispersion factor of 20CrMnTi steel material, combined with SN curves for fatigue life prediction. By precisely considering these material parameters, the fatigue characteristics of gears under actual working conditions can be reflected. This allows the method to not only cope with various complex working conditions but also better adapt to the variations in gear materials of different tram main reducers. Finally, this invention combines the linear fatigue cumulative damage method and stress amplitude frequency spectrum to provide a comprehensive analytical tool for predicting gear fatigue life. By analyzing the cumulative damage of each stress cycle, the damage accumulation process of gears under long-term operation can be assessed, thereby predicting their fatigue life. This method not only has high prediction accuracy but also provides a scientific basis for tram maintenance and repair planning, extending equipment service life and reducing maintenance costs. Attached Figure Description

[0075] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0076] Figure 1 A flowchart illustrating the method for predicting the life of trolley main reducer gears based on CLTC-P operating conditions provided in this embodiment of the invention.

[0077] Figure 2 The input speed of the primary gear of the main reducer provided in this embodiment of the invention;

[0078] Figure 3The input speed of the secondary gear of the main reducer provided in the embodiment of the present invention;

[0079] Figure 4 The average frequency of the first-stage gear contact stress amplitude provided in the embodiments of the present invention;

[0080] Figure 5 The average frequency of the contact stress amplitude of the secondary gear provided in the embodiments of the present invention. Detailed Implementation

[0081] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0082] like Figure 1-5 As shown in some embodiments of this application, this embodiment provides a method for predicting the life of the main reducer gears of a trolleybus based on the CLTC-P (China Light Vehicle Driving Cycle-Passenger Car) operating condition, including:

[0083] Step S100: Obtain the vehicle parameters of CLTC-P and the speed-time series of CLTC-P under various road conditions.

[0084] Specifically, acquiring the vehicle parameters of CLTC-P and its speed-time series under various road conditions includes: discretely sampling the standard speed-time curve of CLTC-P based on a preset sampling time interval to obtain a set of discrete speed-time points; performing preliminary smoothing processing on the discrete sampling points based on a dynamic noise adaptive filtering algorithm to reduce the influence of measurement noise and abrupt changes; interpolating the set of discrete speed-time points using a multi-resolution linear interpolation method to generate a high-precision continuous speed-time curve; and denoising the interpolated speed-time curve and introducing an adaptive threshold filter. The wave method balances noise suppression and signal fidelity, while unifying the time step of the denoised speed-time curve. Based on the CLTC-P working condition definition and intelligent clustering algorithm, the processed speed-time curve is segmented into low-speed, medium-speed, high-speed, and idling ranges. Abnormal acceleration and deceleration segments are automatically identified, and speed-time series are established for each road condition. The working condition prediction module based on machine learning predicts future working condition change trends based on historical speed-time series characteristics and dynamically corrects the generated speed-time series to enhance the adaptability of the series to actual driving behavior and the prediction accuracy.

[0085] Understandably, by discretely sampling the CLTC-P standard speed-time curve based on a preset sampling time interval, a set of discrete speed-time points is obtained, establishing the basic data framework for vehicle speed. To reduce the impact of measurement noise and abrupt changes, a dynamic noise adaptive filtering algorithm is introduced to initially smooth the discrete sampling points, thereby ensuring the continuity and stability of the speed signal. Subsequently, a multi-resolution linear interpolation method is used to interpolate the set of discrete speed-time points, generating a high-precision continuous speed curve to obtain finer-grained speed information, providing accurate input for subsequent operating condition analysis. Secondly, to further enhance the reliability of the signal, the interpolated speed-time curve is denoised, and an adaptive threshold filtering method is used to balance noise suppression and signal fidelity, while unifying the time step after denoising to ensure the standardization and consistency of data processing. Based on this, combined with the CLTC-P operating condition definition and intelligent clustering algorithm, the speed curve is segmented into low-speed, medium-speed, high-speed, and idle speed ranges, and abnormal acceleration / deceleration segments are automatically identified, realizing the extraction of operating condition features and structured representation of speed data. Finally, by introducing a machine learning-based operating condition prediction module, historical speed-time series features are used to predict future operating condition trends, and the generated speed-time series is dynamically corrected, thereby enhancing the series' adaptability to actual driving behavior and its prediction accuracy. This process enables the speed-time series not only to reflect the current driving state of the vehicle but also to make reasonable predictions about future operating conditions, providing accurate and reliable input data for subsequent torque calculations and gear fatigue life predictions.

[0086] As can be seen, the discrete point set of speed-time is obtained by discretely sampling the CLTC-P standard speed-time curve based on a preset sampling time interval. Secondly, a dynamic noise adaptive filtering algorithm is introduced into the discrete point set for preliminary smoothing to reduce measurement noise and the influence of abrupt changes. Simultaneously, a multi-resolution linear interpolation method is used to interpolate the smoothed speed point set, generating a high-precision continuous speed curve. The interpolated speed curve is further denoised, and an adaptive threshold filtering method is used to balance noise suppression and signal fidelity, while unifying the time step. Based on the CLTC-P operating condition definition and intelligent clustering algorithm, the processed speed curve is segmented into low-speed, medium-speed, high-speed, and idle speed ranges, and abnormal acceleration / deceleration segments are automatically identified. Finally, a machine learning-based operating condition prediction module is introduced to predict future operating condition changes based on historical speed-time series characteristics and dynamically correct the generated sequence. Details are shown in Table 1.

[0087] Table 1. Examples of velocity-time series processing

[0088]

[0089] Based on Table 1 above, it can be seen that, firstly, the CLTC-P standard speed-time curve is discretely sampled based on a preset sampling time interval to obtain a discrete set of speed-time points. To reduce the influence of measurement noise and abrupt changes, a dynamic noise adaptive filtering algorithm is introduced to perform preliminary smoothing of the discrete sampling points, ensuring the continuity and stability of the speed signal. Secondly, the speed-time discrete point set after preliminary smoothing is interpolated using a multi-resolution linear interpolation method to generate a high-precision continuous speed curve, thereby obtaining finer-grained speed information and providing accurate input for subsequent operating condition analysis. Thirdly, the interpolated speed-time curve is further denoised, and an adaptive threshold filtering method is used to balance noise suppression and signal fidelity. Simultaneously, the time step of the denoised speed-time curve is standardized to ensure the standardization and consistency of data processing. Furthermore, combining the CLTC-P operating condition definition and intelligent clustering algorithm, the processed speed-time curve is segmented into low-speed, medium-speed, high-speed, and idle speed ranges, while automatically identifying abnormal acceleration and deceleration segments, realizing the extraction of operating condition features and structured representation of speed data. Finally, the machine learning-based operating condition prediction module uses historical speed-time series features to predict future operating condition trends and dynamically corrects the generated speed-time series, thereby enhancing the series' adaptability to actual driving behavior and improving prediction accuracy. The speed-time series generated through these steps not only reflects the current vehicle driving state but also provides reasonable estimates of future operating conditions, offering accurate and reliable input data for subsequent calculations of the main reducer's input torque and gear fatigue life prediction.

[0090] Step S200: Based on the vehicle driving resistance balance equation, vehicle parameters and speed-time series, determine the torque history of the main reducer input during the preset time period, and determine the change history of the contact stress between the first-stage gear and the second-stage gear of the main reducer during the preset time period according to the torque history during the preset time period and the gear contact strength verification method.

[0091] Specifically, when determining the torque history of the main reducer input during a preset time period based on the vehicle driving resistance balance equation, vehicle parameters, and speed-time series, the process includes: establishing a vehicle driving resistance balance equation based on vehicle dynamics, and dynamically correcting the rolling resistance coefficient and air resistance coefficient based on an adjustable resistance coefficient model; obtaining the vehicle acceleration at each moment based on the speed-time series under various road conditions using an adaptive high-order numerical differential method, and correcting the acceleration peak value using filtering; determining the total driving resistance at each moment based on vehicle parameters, rolling resistance coefficient, air resistance coefficient, frontal area, wheel rolling radius, and road slope angle, and correcting the total driving resistance value based on temperature, wind speed, and road surface friction coefficient using an environmental factor compensation module; determining the input torque at the main reducer input at each moment based on the total driving resistance, wheel rolling radius, main reduction ratio, and transmission efficiency, and generating a dataset of torque changes over time based on an adaptive smoothing algorithm; and obtaining the input torque history for the corresponding time period from the torque dataset based on the preset time period, and dynamically correcting the future torque change trend based on a machine learning prediction module.

[0092] Specifically, when determining the input torque at the input end of the main reducer at each moment based on the total driving resistance, wheel rolling radius, main reduction ratio, and transmission efficiency, the following steps are taken: obtaining the rolling resistance, air resistance, slope resistance, and acceleration resistance of the vehicle at each moment, and determining the total driving resistance of the vehicle at each moment based on the rolling resistance, air resistance, slope resistance, and acceleration resistance of the vehicle at each moment; obtaining the torque acting on the wheel side at each moment based on the total driving resistance and the wheel rolling radius of the vehicle at each moment; and obtaining the input torque Tt at the input end of the main reducer at each moment based on the relationship between the torque acting on the wheel side at each moment and the main reduction ratio and transmission efficiency, and based on formula (1):

[0093] (1)

[0094] in, Zt Let be the total driving resistance between different times, r be the wheel rolling radius, io be the principal reduction ratio, and η be the transmission efficiency.

[0095] Understandably, establishing a driving resistance balance equation based on vehicle dynamics and dynamically correcting the rolling resistance and air resistance coefficients using an adjustable resistance coefficient model allows the calculated driving resistance to more accurately reflect the impact of actual road conditions and environmental changes on vehicle resistance. For example, in congested urban traffic scenarios, frequent starts and stops lead to significant changes in rolling resistance and air resistance. The adjustable resistance coefficient model can dynamically adjust the resistance coefficient based on vehicle speed and acceleration, making the resistance calculation closer to actual conditions. Simultaneously, for speed-time series under different road conditions, an adaptive high-order numerical differential method is used to obtain vehicle acceleration, and peak values ​​are corrected through filtering, ensuring the stability and accuracy of acceleration calculations and avoiding the impact of errors caused by sudden acceleration and deceleration on subsequent torque calculations. Furthermore, this method combines vehicle parameters, resistance coefficients, frontal area, wheel rolling radius, and road slope angle to calculate the total driving resistance at each moment. An environmental factor compensation module is introduced to correct the total driving resistance based on temperature, wind speed, and road surface friction coefficient, ensuring that the resistance calculation fully considers the impact of the external environment on vehicle dynamics. For example, in mountainous or steep road scenarios, changes in gradient significantly impact driving resistance. The environmental factor compensation module can correct vehicle resistance in real time to ensure that the calculated torque history matches actual operating conditions. By combining the total driving resistance at each moment with wheel parameters, final drive ratio, and transmission efficiency, the torque acting on the input end of the final drive can be accurately obtained, providing a reliable basis for the vehicle's power output under different operating conditions. Furthermore, after generating a dataset of input torque variations over time, this method further employs an adaptive smoothing algorithm to process the torque curve, eliminating potential instantaneous disturbances. Simultaneously, a machine learning prediction module dynamically corrects future torque change trends. For instance, in continuous acceleration scenarios on highways, the machine learning module can predict short-term torque fluctuations based on historical speed-time series, correcting the calculated torque history to better reflect actual driving behavior. This process ensures that the final drive input torque history not only accurately reflects the current vehicle operating conditions but also predicts future torque changes, providing reliable and dynamically adjustable input data for subsequent gear contact stress analysis and fatigue life assessment, thereby improving the accuracy and adaptability of gear life prediction.

[0096] It can be seen that a vehicle driving resistance balance equation is established based on vehicle dynamics, and the rolling resistance coefficient and air resistance coefficient are dynamically corrected based on an adjustable resistance coefficient model to adapt to changes in different road and environmental conditions. For example, rolling resistance dominates when driving at low speeds on urban roads, while air resistance dominates when cruising at a constant speed on highways. Secondly, based on the speed-time series of different road segments under the CLTC-P condition, an adaptive high-order numerical differential method is used to obtain the vehicle acceleration at each moment, and a filtering algorithm is used to correct peak values, suppressing abnormal fluctuations in acceleration values ​​and ensuring the smoothness and accuracy of acceleration data. For example, in urban road conditions with frequent starts and stops at traffic lights, calculation errors caused by instantaneous acceleration and deceleration can be effectively avoided. Thirdly, by combining vehicle parameters, rolling resistance coefficient, air resistance coefficient, frontal area, wheel rolling radius, and road slope angle, the total driving resistance at each moment is determined, and the environmental factor compensation module corrects for temperature, wind speed, and road surface friction coefficient. For example, in mountainous steep slope road conditions, the total driving resistance will increase due to the increased slope, and the compensation module dynamically adjusts the resistance calculation. Then, based on the total driving resistance, wheel rolling radius, main reduction ratio, and transmission efficiency at each moment, the torque value at the input end of the main reducer at each moment is calculated, and a dataset of torque changing over time is generated through an adaptive smoothing algorithm to reduce the impact of transient disturbances. The future torque change trend is dynamically corrected through a machine learning prediction module, so that the generated torque history can more realistically reflect the torque demand of the vehicle under different road conditions and driving behaviors. For example, Table 2. Finally, based on the input torque history obtained by formula (1), combined with the gear contact strength verification method, the contact stress change history between the first-stage gear and the second-stage gear of the main reducer during a preset period is further determined, providing data input for gear fatigue life prediction.

[0097] Table 2. Input Parameters for Example CLTC-P Operating Condition:

[0098]

[0099] Step S300: Based on the growth curve method, perform load spectrum statistics on the time change history of contact stress to obtain the cycle frequency distribution corresponding to each stress amplitude between the first-stage gear and the second-stage gear.

[0100] Specifically, based on the torque history of the preset time period and the gear contact strength verification method, when determining the change history of the contact stress between the first-stage gear and the second-stage gear of the main reducer within the preset time period, the following steps are taken: based on the total driving resistance and gear geometric parameters at each moment, and based on formula (2), the dynamic circumferential force on the pitch circle is determined:

[0101] (2)

[0102] in, FtThe dynamic circumferential force on the pitch circle is given by d, where d is the pitch circle diameter of the pinion. Based on the end face pressure angle between the first-stage and second-stage gears, the helix angle of the base cylinder, and formula (3), the node region coefficient between the first-stage and second-stage gears is obtained.

[0103] (3)

[0104] in Z H This is the node region coefficient between the first-stage and second-stage gears. α t The end face pressure angle, β b Based on the helix angle of the base cylinder, The end face meshing angle is given; based on the elastic modulus of the first gear material, the elastic modulus of the second gear material, the Poisson's ratio of the first gear and the Poisson's ratio of the second gear, and formula (4), the material elastic coefficient between the first-stage gear and the second-stage gear is determined:

[0105] (4)

[0106] in, Z E The elastic modulus of the material between the first-stage gear and the second-stage gear. E 1 Let be the elastic modulus of the gear material of the first gear. E 2 The elastic modulus of the gear material for the second gear. v 1 Let the Poisson's ratio be the first gear. v 2 Let be the Poisson's ratio of the second gear; based on the end face overlap and longitudinal overlap between the first and second gears and formula (5), determine the overlap ratio and helix angle coefficient between the first and second gears:

[0107] (5)

[0108] in, Z εβ This refers to the overlap ratio and helix angle coefficient between the first-stage and second-stage gears. ε α The overlap ratio of the end faces between the first-stage gear and the second-stage gear. ε β This refers to the longitudinal overlap between the first-stage and second-stage gears. βThe helix angle of the pinion is given. Based on the dynamic circumferential force on the pitch circle, the node region coefficient between the first and second gears, the material elastic coefficient, and the overlap ratio and helix angle coefficient, the instantaneous contact stress between the first and second gears is determined. Based on the instantaneous contact stress between the first and second gears of the main reducer within a preset time period, the variation history of the contact stress between the first and second gears within the preset time period is generated.

[0109] Specifically, obtaining the pitch circle diameter of the pinion includes: obtaining the normal module, helix angle, and number of teeth of the pinion, and obtaining the pitch circle diameter of the pinion based on the normal module, helix angle, and number of teeth of the first-stage gear and formula (6):

[0110] (6)

[0111] in, m n Let be the normal module of the pinion. β The helix angle of the pinion. z This refers to the number of teeth.

[0112] Specifically, when determining the instantaneous contact stress between the first and second gears based on the dynamic circumferential force on the pitch circle, the node region coefficient between the first and second gears, the material elastic coefficient, the overlap ratio and helix angle coefficient, and formula (7), the following are included:

[0113] (7)

[0114] in, σH The instantaneous contact stress between the first-stage gear and the second-stage gear. b The tooth widths of the first and second gears. K A For the use factor, K v This is the dynamic load factor. K Hβ The tooth load distribution factor is the tooth direction load distribution factor. K Hα This is the inter-tooth load distribution coefficient.

[0115] Specifically, when performing load spectrum statistics on the time variation history of contact stress based on the growth curve method to obtain the cycle frequency distribution corresponding to each stress amplitude between the first-stage and second-stage gears, the process includes: performing extreme value analysis on the contact stress-time series within a preset time period and identifying all stress peaks and valleys to form a complete stress cycle unit; obtaining the stress amplitude and average stress of each stress cycle unit, and classifying the stress amplitude according to a preset stress level range to establish a 16-level stress amplitude level classification standard; and counting the number of cycles corresponding to each stress level to generate the cycle frequency distribution corresponding to each stress amplitude between the first-stage and second-stage gears.

[0116] Understandably, the dynamic circumferential force between the first and second gears of the main reducer is calculated based on the relationship between vehicle driving resistance and gear geometry. By combining the vehicle's driving resistance with the gear's geometry (such as the pitch circle diameter), the dynamic force acting on the gear can be obtained. This force is a key factor affecting gear contact stress because it directly determines the stress on the gear under actual operating conditions. The dynamic circumferential force reflects the vehicle's loading on the gear under different operating conditions, further affecting the change process of gear contact stress. Next, the node region coefficient is obtained through the gear's end face pressure angle and base cylinder helix angle. The node region coefficient reflects the change in the gear contact area, which is crucial for accurately calculating the contact stress between gears. The end face pressure angle and helix angle are important parameters determining the shape of the gear contact surface and load distribution, and the node region coefficient further affects the magnitude and distribution of gear contact stress. Therefore, by correctly calculating this coefficient, the mechanical properties of the gear contact surface can be described more accurately. Then, the material elastic coefficient between the gears is determined through the elastic modulus and Poisson's ratio of the gear material. The elastic characteristics of the gear determine the degree of material deformation during the stress process. The elastic modulus reflects a material's resistance to deformation, while Poisson's ratio characterizes the lateral and longitudinal deformation relationship of a material under stress. Combining these two factors allows for the calculation of the material elastic coefficients between gears, which is crucial for calculating gear contact stress, as contact stress is closely related to the elastic properties of the gear material. Furthermore, the contact ratio and helix angle coefficient between gears are determined based on their overlap ratio and helix angle coefficient. These two coefficients are two important factors affecting gear contact. The overlap ratio affects the contact range between gear tooth surfaces, while the helix angle coefficient determines the slope of the gear contact surface and the load distribution. Accurate calculation of these two coefficients allows for a more precise description of the mechanical characteristics of gear contact, thus providing more accurate parameters for calculating contact stress. Subsequently, by comprehensively considering dynamic circumferential force, node region coefficient, material elastic coefficient, and contact ratio and helix angle coefficient, the instantaneous contact stress between the gears of the main reducer is calculated. This process calculates instantaneous contact stress by combining multiple factors, including gear geometry, material properties, and operating conditions. This comprehensive approach allows for an accurate description of the instantaneous stress changes experienced by the gear during operation, providing the necessary data foundation for subsequent fatigue life analysis. Finally, by statistically analyzing the load spectrum of the contact stress over time using the growth curve method, the stress amplitude and cycle frequency distribution of the gear are obtained. The growth curve method can be used to statistically analyze the extreme values ​​during stress changes, thereby analyzing the frequency of occurrence of each stress amplitude. Based on this, a stress amplitude level classification standard is established through stress amplitude grading, and the number of cycles for each level is statistically analyzed.This process helps to generate a detailed load spectrum, providing stress amplitude and cycle frequency distribution data for subsequent fatigue analysis, thereby further predicting the fatigue life of the gear.

[0117] Step S400: Based on the Goodman linear life correction method, the stress with non-zero average value in the cycle frequency corresponding to each stress amplitude is converted into equivalent symmetrical cyclic stress, and a stress amplitude frequency spectrum is established based on the cycle frequency distribution corresponding to each stress amplitude and the equivalent symmetrical cyclic stress.

[0118] Specifically, this method transforms non-zero average stress into equivalent symmetrical cyclic stress. In actual operating conditions, the stress amplitude of gears often includes both zero-mean and non-zero-mean values, while fatigue life prediction is usually based on the analysis of symmetrical cyclic stress. The Goodman correction method corrects the non-zero average stress, transforming it into equivalent symmetrical cyclic stress, thus converting asymmetric stress into symmetrical stress for subsequent fatigue life assessment. Next, a stress amplitude frequency spectrum is established by combining the cyclic frequency distribution corresponding to each stress amplitude with the equivalent symmetrical cyclic stress. This process involves statistically analyzing different levels of stress amplitude and their frequencies, and combining this with the equivalent symmetrical cyclic stress to form a spectrum reflecting the stress distribution. This spectrum provides the distribution of different stress amplitudes at different cycle numbers, thus providing more accurate input data for subsequent fatigue analysis. Through this spectrum, the main stress cycle patterns and their corresponding frequencies can be identified, providing crucial information for fatigue damage accumulation models. Finally, based on the established stress amplitude frequency spectrum, fatigue damage analysis methods (such as the Miner linear cumulative damage method) can be further applied to assess the fatigue life of the gear. This frequency spectrum provides fundamental data for assessing the cumulative damage of gears under different stress amplitudes, thus making fatigue life prediction more accurate and reliable. By combining the stress amplitude frequency spectrum with the SN curve, the fatigue life of gears can be accurately predicted based on their stress history in actual use, providing a scientific basis for gear design and maintenance.

[0119] Understandably, the application of the Goodman linear life correction method effectively transforms non-zero average stress into equivalent symmetrical cyclic stress. In actual working conditions, gears are often subjected to asymmetrical stress loading, and traditional fatigue life prediction methods struggle to accurately handle such complex stress situations. The Goodman correction method, by standardizing the stress state, allows asymmetrical stress to be transformed into symmetrical stress, ensuring the reliability of stress analysis and providing a unified computational basis for subsequent fatigue damage assessment. Secondly, by combining the cyclic frequency distribution of each stress amplitude with the equivalent symmetrical cyclic stress, a stress amplitude frequency spectrum is established. This spectrum provides a detailed distribution of stress amplitude and its frequency of occurrence, offering more accurate data support for fatigue life prediction. Through this distribution, common stress cycling patterns in gear operation can be accurately captured, and the gear's stress condition can be analyzed at different frequencies, helping to identify potential fatigue failure risks and improve prediction accuracy. Finally, based on the established stress amplitude frequency spectrum, combined with fatigue damage analysis methods (such as the Miner linear cumulative damage method), the fatigue life of gears can be assessed more scientifically. This allows fatigue life prediction to move beyond simplistic assumptions and instead be based on stress cycles under real-world conditions, providing more accurate fatigue damage analysis results. Furthermore, by incorporating SN curves, key factors influencing fatigue life can be identified early in gear design and maintenance, optimizing design schemes and improving gear reliability, ultimately reducing gear failure rates and maintenance costs.

[0120] Step S500: Based on the effective stress concentration factor, size factor, surface finish factor and dispersion factor of 20CrMnTi steel material, obtain the SN curve corresponding to the gear of the main reducer.

[0121] Specifically, the stress concentration factor (SN) curve of the gear is corrected by using the effective stress concentration factor, size factor, surface finish factor, and dispersion factor of 20CrMnTi steel. These correction factors quantify the impact on fatigue performance based on the actual physical properties and processing characteristics of the gear material. These factors allow for a more accurate consideration of the effects of stress concentration, dimensional differences, surface treatment, and material dispersion on fatigue life under different operating conditions, resulting in a more realistic SN curve. Secondly, the SN curve is a graph describing the relationship between material fatigue life and stress amplitude. By combining the above correction factors, the SN curve can more accurately reflect the fatigue behavior of 20CrMnTi steel under actual working conditions. These factors supplement the influence of local stress concentration, which is ignored by the standard SN curve, making the fatigue life prediction closer to actual working conditions and material properties. Each factor considers the influence of internal and surface conditions on fatigue strength; for example, stress concentration usually leads to a decrease in material load-bearing capacity, while surface treatment processes can improve the fatigue resistance of the gear. Finally, using these corrected SN curves for fatigue life prediction allows for more accurate and reliable gear design. By correcting the SN curve of 20CrMnTi steel, the fatigue life of gears under different operating conditions can be predicted more accurately, providing a scientific basis for gear reliability design. Considering the influence of materials and processing technology, gear design can be optimized in terms of fatigue resistance, thereby improving gear service life and reducing maintenance costs.

[0122] Step S600: Based on the linear fatigue cumulative damage theory, stress amplitude frequency spectrum and SN curve, determine the fatigue life prediction result of the main reducer gear.

[0123] Specifically, when determining the predicted fatigue life of the main reducer gear based on the linear fatigue cumulative damage theory, stress amplitude frequency spectrum, and SN curve, the following steps are taken: obtaining the SN curve corresponding to the main reducer gear material, and determining the fatigue life of the gear based on the stress amplitude frequency spectrum and equivalent symmetrical cyclic stress, wherein: the damage degree of the main reducer gear material is determined according to each stress amplitude and formula (8).

[0124] (8)

[0125] in, D i Damage degree of the main reducer gear material, n i N represents the number of cycles at the i-th stress amplitude. i Let be the lifespan under the i-th stress amplitude; based on the linear fatigue cumulative damage method, the damage degree of each stress amplitude is accumulated, and the total damage degree of the main reducer gear material is determined based on formula (9):

[0126] (9)

[0127] in, R The total damage degree of the main reducer gear material is determined. Based on the relationship between the total damage degree of the main reducer gear material and the preset total damage degree, it is determined whether the main reducer gear has reached its fatigue life: when the total damage degree of the main reducer gear material is higher than or equal to the preset total damage degree, the main reducer gear is determined to have reached its fatigue life; when the total damage degree of the main reducer gear material is lower than the preset total damage degree, the main reducer gear has not reached its fatigue life. The fatigue warning level of the main reducer gear is determined based on the damage degree difference between the total damage degree of the main reducer gear material and the preset total damage degree.

[0128] Specifically, when determining the fatigue warning level of the main reducer gear based on the damage difference between the total damage degree of the main reducer gear material and a preset total damage degree, the following steps are taken: The fatigue warning level of the main reducer gear is determined based on the relationship between the damage difference and a configured first and second preset damage difference: when the damage difference is lower than the first preset damage difference, the fatigue warning level of the main reducer gear is determined to be high-risk; when the damage difference is higher than or equal to the first preset damage difference and lower than the second preset damage difference, the fatigue warning level of the main reducer gear is determined to be medium-risk; when the damage difference is higher than or equal to the second preset damage difference, the fatigue warning level of the main reducer gear is determined to be low-risk; wherein, the first preset damage difference is less than the second preset damage difference.

[0129] Understandably, the combination of the linear fatigue cumulative damage method and the stress amplitude frequency spectrum with the SN curve is used to predict the fatigue life of the main reducer gear. The linear fatigue cumulative damage method assesses the fatigue damage of the gear material under long-term load by accumulating the damage degree under different stress amplitudes. For each stress amplitude, the corresponding number of cycles and life are calculated to obtain the damage degree at each stress amplitude. Then, by summing the damage degrees of all stress amplitudes, the total damage degree of the gear is obtained, providing a basis for further determining the fatigue life of the gear. Secondly, based on the analysis of the stress amplitude frequency spectrum and equivalent symmetrical cyclic stress, the fatigue life of the gear material can be effectively predicted. The stress amplitude frequency spectrum provides the frequency of occurrence of the gear at different stress amplitudes. Combined with the SN curve, the impact of each stress amplitude on the gear life can be accurately assessed. By calculating the damage degree corresponding to each stress amplitude, the linear fatigue cumulative damage method is used to accumulate the damage degrees of different stress amplitudes, thereby obtaining the overall fatigue damage state of the gear and further determining whether fatigue life has been reached. Then, by comparing with the preset total damage degree, it can be determined whether the gear has reached its fatigue life. When the total damage degree of a gear exceeds or equals a preset total damage degree, it indicates that the gear has reached its fatigue life and needs replacement or repair. Conversely, when the total damage degree is lower than the preset total damage degree, the gear is considered not to have reached its fatigue life, and the fatigue risk level is assessed using the damage degree difference. This process categorizes gear risks by setting different damage degree difference thresholds. Finally, the fatigue warning level of the gear is determined based on the relationship between the damage degree difference and the preset damage degree difference thresholds. Different preset damage degree difference values ​​help to more accurately assess the fatigue risk level of the gear. When the damage degree difference is small, below the first preset difference, the gear is at a high risk level; when the difference is between the two, the gear is at a medium risk level; and when the damage degree difference is large, exceeding the second preset difference, it is at a low risk level. This warning mechanism can predict the fatigue degree of gears in advance and take corresponding maintenance or replacement measures according to the risk level, thereby ensuring the safe operation of the equipment.

[0130] In the above embodiments, by introducing vehicle driving parameters and speed-time series under the CLTC-P condition, the dynamic operating conditions of the tram in actual operation are fully considered. This method can accurately simulate the operating state of the tram under different conditions, such as low-speed, medium-speed, high-speed, and idling ranges, solving the problem of neglecting actual road condition changes common in traditional methods. Through these dynamic data, the changes in gear contact stress during actual driving can be simulated more accurately, thereby effectively improving the accuracy of gear life prediction. In addition, this invention uses the growth curve method to perform load spectrum statistics on the time change history of contact stress and analyzes it based on the cycle number distribution of different stress amplitudes. This method can make more detailed predictions of gear fatigue life based on stress amplitude, avoiding the overly simplified stress distribution assumptions in traditional methods. By establishing a stress amplitude frequency spectrum and combining it with the Goodman linear life correction method, the asymmetric stress in actual operation can be transformed into symmetric stress, thereby further improving the accuracy of life prediction. In terms of materials, this invention uses the effective stress concentration factor, size factor, surface finish factor, and dispersion factor of 20CrMnTi steel material, combined with SN curves for fatigue life prediction. By precisely considering these material parameters, the fatigue characteristics of gears under actual working conditions can be reflected. This allows the method to not only cope with various complex working conditions but also better adapt to the variations in gear materials of different tram main reducers. Finally, this invention combines the linear fatigue cumulative damage method and stress amplitude frequency spectrum to provide a comprehensive analytical tool for predicting gear fatigue life. By analyzing the cumulative damage of each stress cycle, the damage accumulation process of gears under long-term operation can be assessed, thereby predicting their fatigue life. This method not only has high prediction accuracy but also provides a scientific basis for tram maintenance and repair planning, extending equipment service life and reducing maintenance costs.

[0131] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0132] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0133] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0134] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0135] 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 it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for predicting the service life of a main reducer gear of an electric vehicle based on CLTC-P operating conditions, characterized in that, The method comprises the following steps: obtaining vehicle parameters of the CLTC-P and speed-time sequences of the CLTC-P under various road conditions; determining a torque history of the main reducer input within a preset time period based on a vehicle running resistance balance equation, vehicle parameters and the speed-time sequences, and determining a change history of the contact stress between the primary gear and the secondary gear of the main reducer within the preset time period according to the torque history of the preset time period and a gear contact strength checking method; statistically analyzing the time change history of the contact stress based on a growth curve method to obtain a cycle frequency distribution corresponding to each stress amplitude between the primary gear and the secondary gear; converting the stress with a non-zero average value in the cycle frequency corresponding to each stress amplitude into an equivalent symmetric cyclic stress according to a Goodman linear life correction method, and establishing a stress amplitude frequency spectrum according to the cycle frequency distribution corresponding to each stress amplitude and the equivalent symmetric cyclic stress; obtaining an S-N curve corresponding to the main reducer gear based on the effective stress concentration coefficient, size coefficient, surface processing coefficient and dispersion coefficient of the 20CrMnTi steel material; determining a fatigue life prediction result of the main reducer gear based on a linear fatigue cumulative damage method, the stress amplitude frequency spectrum and the S-N curve.

2. The CLTC-P operating condition-based electric vehicle main reducer gear life prediction method according to claim 1, characterized in that, When obtaining the vehicle parameters of the CLTC-P and the speed-time sequences of the CLTC-P under various road conditions, the method comprises the following steps: discretely sampling the CLTC-P standard speed-time curve based on a preset sampling time interval, and obtaining a speed-time discrete point set; and preliminarily smoothing the discrete sampling points based on a dynamic noise adaptive filtering algorithm to reduce the influence of measurement noise and abrupt points; interpolating the speed-time discrete point set based on a multi-resolution linear interpolation method to generate a high-precision continuous speed-time curve; performing denoising processing on the interpolated speed-time curve, introducing an adaptive threshold filtering method to balance noise suppression and signal fidelity, and unifying the time step of the denoised speed-time curve; based on the CLTC-P condition definition and an intelligent clustering algorithm, marking the processed speed-time curve according to the low-speed interval, the medium-speed interval, the high-speed interval and the idle speed interval, automatically identifying abnormal acceleration and deceleration sections, and establishing the speed-time sequences under various road conditions; based on a condition prediction module of machine learning, predicting the future condition change trend according to the historical speed-time sequence characteristics, dynamically correcting the generated speed-time sequences to enhance the adaptability and prediction accuracy of the sequences to the actual driving behavior.

3. The CLTC-P operating condition-based electric vehicle main reducer gear life prediction method according to claim 2, characterized in that, When determining the torque history of the main reducer input within a preset time period based on a vehicle running resistance balance equation, vehicle parameters and the speed-time sequences, the method comprises the following steps: establishing a vehicle running resistance balance equation based on vehicle dynamics relationship, and dynamically correcting the rolling resistance coefficient and the air resistance coefficient based on an adjustable resistance coefficient model; based on the speed-time sequences under various road conditions, obtaining the vehicle acceleration at each time based on an adaptive high-order numerical differentiation method, and filtering and correcting the acceleration peak value based on a filtering correction method; Determine the total driving resistance at each time based on vehicle parameters, rolling resistance coefficient, air resistance coefficient, wind area, wheel rolling radius and road slope angle, and correct the total driving resistance value according to temperature, wind speed and road surface friction coefficient based on the environmental factor compensation module; Determine the input torque of the main reducer input end at each time according to the total driving resistance, wheel rolling radius, main reduction ratio and transmission efficiency at each time, and generate a torque data set changing with time based on the adaptive smoothing algorithm; According to the preset time period, the input torque history of the corresponding time period is obtained from the torque data set, and the future torque change trend is dynamically corrected based on the machine learning prediction module.

4. The CLTC-P operating condition-based electric vehicle main reducer gear life prediction method according to claim 3, characterized in that, When determining the input torque of the main reducer input end at each time according to the total driving resistance, wheel rolling radius, main reduction ratio and transmission efficiency at each time, it includes: Obtain the rolling resistance, air resistance, slope resistance and acceleration resistance of the vehicle at each time, and determine the total driving resistance of the vehicle at each time according to the rolling resistance, air resistance, slope resistance and acceleration resistance of the vehicle at each time; According to the total driving resistance at each time and the wheel rolling radius of the vehicle, the torque acting on the wheel side at each time is obtained; According to the relationship between the torque acting on the wheel side at each time and the main reduction ratio and transmission efficiency, and based on formula (1), the input torque Tt of the main reducer input end at each time is obtained: （1） wherein, Zt is the total travel resistance between each time, r is the wheel rolling radius, i0is the main reduction ratio, and η is the transmission efficiency.

5. The CLTC-P operating condition-based electric vehicle main reducer gear life prediction method according to claim 4, characterized in that, When determining the contact stress change history between the primary gear and the secondary gear of the main reducer within the preset time period according to the torque history of the preset time period and the gear contact strength checking method, it includes: Based on the total driving resistance and gear geometric parameters between each time, and based on formula (2), the dynamic circumferential force on the reference circle is determined: （2） wherein, Ft is the dynamic circumferential force on the pitch circle, d is the pitch circle diameter of the pinion. Based on the end face pressure angle and the base cylinder helix angle between the primary gear and the secondary gear, and formula (3), the node area coefficient between the primary gear and the secondary gear is obtained: （3） wherein Z H is a node area coefficient between the primary gear and the secondary gear, α t is an end face pressure angle, β b is a base cylinder helix angle, is an end face engagement angle; Based on the elastic modulus of the gear material of the first gear, the elastic modulus of the gear material of the second gear, the Poisson's ratio of the first gear and the Poisson's ratio of the second gear, and formula (4), the material elastic coefficient between the primary gear and the secondary gear is determined: （4） wherein, Z E E1 is the modulus of elasticity of the material between the first and second gears, E 1 E1 is the modulus of elasticity of the material between the first and second gears, E 2 E2 is the modulus of elasticity of the material of the second gear, v 1 v1 is the Poisson's ratio of the first gear, v 2 v2 is the Poisson's ratio of the second gear. According to the end face coincidence degree and the longitudinal coincidence degree between the primary gear and the secondary gear, and formula (5), the coincidence degree and helix angle coefficient between the primary gear and the secondary gear are determined: （5） wherein, Z εβ is a coefficient of the degree of coincidence and the helix angle between the first gear and the second gear, According to the dynamic circumferential force on the reference circle, the node area coefficient, the material elastic coefficient and the coincidence degree and helix angle coefficient between the primary gear and the secondary gear, the instantaneous contact stress between the primary gear and the secondary gear is determined; α is a coefficient of the end face coincidence between the first gear and the second gear, According to the instantaneous contact stress between the primary gear and the secondary gear of the main reducer within the time step of the preset time period, the change history of the contact stress between the primary gear and the secondary gear within the preset time period is generated. β is a coefficient of the longitudinal coincidence between the first gear and the second gear, β is a helix angle of the pinion. When obtaining the reference circle diameter of the pinion, it includes: Obtain the normal modulus, helix angle and tooth number of the pinion, and obtain the reference circle diameter of the pinion according to the normal modulus, helix angle and tooth number of the primary gear and formula (6):

6. The CLTC-P operating condition-based electric vehicle main reducer gear life prediction method according to claim 5, characterized in that, ​ ​ （6） wherein, m n is the normal module of the pinion, β is the helix angle of the pinion, z is the number of teeth.

7. The CLTC-P operating condition-based electric vehicle main reducer gear life prediction method according to claim 6, characterized in that, According to the dynamic circumferential force on the reference circle, the node area coefficient between the primary gear and the secondary gear, the material elastic coefficient, and the relationship between the overlap degree and the spiral angle coefficient, and formula (7), the instantaneous contact stress between the primary gear and the secondary gear is determined, including: （7） wherein, σH is the instantaneous contact stress between the primary and secondary gears, b is the tooth width of the primary and secondary gears, K A is the usage factor, K v is the dynamic load factor, K Hβ is the tangential load distribution factor, K Hα is the intertooth load distribution factor.

8. The CLTC-P operating condition based electric vehicle main reducer gear life prediction method of claim 1, wherein, Based on the growth curve method, the time variation of the contact stress is statistically loaded, and the cycle frequency distribution corresponding to each stress amplitude between the primary gear and the secondary gear is obtained, including: Performing extreme value analysis on the contact stress-time sequence in the preset period, and identifying all stress peaks and stress valleys to form a complete stress cycle unit; Obtaining the stress amplitude and average stress of each stress cycle unit, and classifying the stress amplitude according to the preset stress level interval to establish a 16-level stress amplitude level classification standard; Statistically counting the cycle times corresponding to each stress level to generate a cycle frequency distribution corresponding to each stress amplitude between the primary gear and the secondary gear.

9. The CLTC-P operating condition based electric vehicle main reducer gear life prediction method of claim 1, wherein, Based on the linear fatigue cumulative damage method, the stress amplitude frequency spectrum, and the S-N curve, the fatigue life prediction result of the main reducer gear is determined, including: Obtaining the S-N curve corresponding to the main reducer gear material, and determining the fatigue life of the gear based on the stress amplitude frequency spectrum and the equivalent symmetric cyclic stress, wherein: According to each stress amplitude and formula (8), the damage degree of the main reducer gear material is determined: （8） wherein, D i n is the damage degree of the main reducer gear material, i N is the number of cycles at the i-th stress amplitude, i n is the life at the i-th stress amplitude; Based on the linear fatigue cumulative damage method, the damage degrees of each stress amplitude are accumulated, and the total damage degree of the main reducer gear material is determined based on formula (9): （9） wherein, R Dtotal is the total damage degree for the main reducer gear material; According to the relationship between the total damage degree of the main reducer gear material and the preset total damage degree, it is determined whether the main reducer gear reaches the fatigue life: When the total damage degree of the main reducer gear material is higher than or equal to the preset total damage degree, it is determined that the main reducer gear reaches the fatigue life; When the total damage degree of the main reducer gear material is lower than the preset total damage degree, it is determined that the main reducer gear has not reached the fatigue life, and according to the damage degree difference between the total damage degree of the main reducer gear material being lower than the preset total damage degree, the fatigue warning level of the main reducer gear is determined.

10. The CLTC-P operating condition-based electric vehicle main reducer gear life prediction method according to claim 9, characterized in that, According to the damage degree difference between the total damage degree of the main reducer gear material being lower than the preset total damage degree, the fatigue warning level of the main reducer gear is determined, including: According to the relationship between the damage degree difference and the first preset damage degree difference and the second preset damage degree difference, the fatigue warning level of the main reducer gear is determined: When the damage degree difference is lower than the first preset damage degree difference, it is determined that the fatigue warning level of the main reducer gear is a high-risk level; When the damage degree difference is higher than or equal to the first preset damage degree difference and lower than the second preset damage degree difference, it is determined that the fatigue warning level of the main reducer gear is a medium-risk level; When the damage degree difference is higher than or equal to the second preset damage degree difference, it is determined that the fatigue warning level of the main reducer gear is a low-risk level; Wherein, the first preset damage degree difference is less than the second preset damage degree difference.

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