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Uniaxial fatigue tests of rubber dumbbell specimens under different mean and amplitude of strain are carried out. An Extreme Learning Machine (ELM) model optimized by Dragonfly Algorithm (DA) is proposed to predict the fatigue life of rubber based on measured rubber fatigue life data. Mean and amplitude of strain and measured rubber fatigue life are taken as input variables and output variables respectively in DA-ELM model. For comparison, genetic algorithm (GA) and particle swarm optimization (PSO) are used to optimize ELM parameters, and GA-ELM and PSO-ELM models are established. The comparison results show that DA-ELM model performs better in predicting the fatigue life of rubber with least dispersion. The coefficients of determination for the training set and test set are 99.47% and 99.12%, respectively. In addition, a life prediction model equivalent strain amplitude as damage parameter is introduced to further highlight the superiority of DA-ELM model.

In order to enhance the efficiency of durability testing of automobile parts, a time-frequency domain accelerated editing method of road load time series of rubber mount on powertrain was discussed. Based on Stockwell Transform method and Accumulative Power Spectral Density, a new time-frequency domain accelerated editing method (ST-APSD) was proposed. The accumulative power spectral density was obtained by ST of the load time series signal of automobile powertrain rubber mounting force which is acquired by the real vehicle in the test field. Based on the accumulative power spectral density, the threshold value was proposed to identify and delete the small damage load fragments, and then the acceleration spectrum was obtained.

This paper proposes a new evaluation method of analyzing stability and design of a controller for an electric power steering (EPS) system. The main purpose of the EPS system’s control design is to ensure a comfortable driving experience of drivers, which mainly depends on the assist torque map. However, the high level of assist gain and its nonlinearity may cause oscillation, divergence and instability to the steering systems. Therefore, an EPS system needs to have an extra stability controller to eliminate the side effect of assist gain on system stability and attenuate the unpleasant vibration. In this paper, an accurate theoretical model is built and the method for evaluating system quality are suggested. The bench tests and vehicle experiments are carried out to verify the theoretical analysis.

The elliptically shaped bearing (ESB) with a rigid, elliptical inner race and a flexible, thin-walled outer race is the most easily damaged core component of harmonic drive. The ESB rotates under cycle load of alternating stress due to its special elliptic structure. Hence, the fault features of ESB such as fatigue spalling and pitting are apt to be concealed by the excitation of impulses caused by alternating between major axis and minor axis. In order to diagnose the fault on raceway surfaces of ESB, a new method of CMWT-FH based on Continuous Morlet Wavelet Transform (CMWT) and FFT-based Hilbert (FH) spectrum analysis is proposed to extract the fault feature.

Automobile rubber isolator was subjected to random load cycle for a long time in the service process, and its main rubber material for vibration isolation was prone to fatigue failure. Since the traditional Miner damage theory overlooked the load randomness, it had a prediction error problem. In order to improve the prediction accuracy of rubber fatigue life, the traditional Miner damage theory was modified by random uncertainty theory to predict the rubber fatigue life under random load. Firstly, the rubber dumbbell-shaped test column, which was vulcanized from rubber materials commonly used in vibration isolators, was taken as the research object. The uniaxial fatigue test of rubber under different strain amplitudes and strain mean values was carried out. Then the fatigue characteristic curve of rubber with equivalent strain amplitude as the damage parameter was established.

Rubber isolators are widely used under random vibrations. In order to predict their fatigue life, a study on the fatigue analysis methodology for rubber isolators is carried out in this paper. Firstly, taking a mount used for isolating air conditioning compressor vibrations as studying example, accelerations versus time of rubber isolator at both sides are acquired for a car under different running conditions. The acceleration in time domain is transformed to frequency domain using the Fourier transform, and the acceleration power spectral density (PSD) is the obtained. Using the PSD as input, fatigue test is carried for the rubber isolator in different temperature and constant humidity conditions. A finite element model of the rubber isolator using ABAQUS is established for estimating fatigue life, and model validity is verified through static characteristic testing. Dynamic responses of the rubber isolator at frequency domain are calculated if a unit load is applied.

The fatigue prediction model of an air spring based on the crack initiation method is established in this study. Taking a rolling lobe air spring with an aluminum casing as the studying example, a finite element model for analyzing force versus displacement is developed. The static stiffness and dimensional parameters of limit positions are calculated and analyzed. The influence of different modeling methods of air springs bellow are compared and analyzed. Static stiffness measurement of an air spring is conducted, and the calculation results and the measured results of the static stiffness are compared. It is shown that the relative error of the measured stiffness and calculated stiffness is within 1%. The Abaqus post-processing stage is redeveloped in Python language.

Battery Run-down under the Electric Vehicle Operation (BREVO) model is a model that links the driver’s travel pattern to physics-based battery degradation and powertrain energy consumption models. The model simulates the impacts of charging behavior, charging rate, driving patterns, and multiple energy management modules on battery capacity degradation. This study implements reinforcement learning (RL) to the simplified BREVO model to optimize drivers’ decisions on charging such as charging rate, charging time, and charging capacity needed. This is done by a reward function that considers both the driver’s daily travel demands and the minimization of battery degradation over a year. It shows that using appropriate charger type (No Charge, Level 1, Level 2, direct-current Fast Charge [DCFC], extreme Fast Charging [xFC]) with an appropriate charging time can reduce battery degradation and total charging cost at the end of the year while satisfying driver’s daily travel demand.

The ball joint with cross groove offers both angular and plunging motion. When transmitting the same torque, the cross groove ball joint is lighter than other plunging Constant Velocity Joints (CVJs). It is crucial for the design of the joint and enhancing the contact fatigue life of the raceway to accurately estimate component loads of the ball joints with cross groove. In this study, the transmission efficiency of the joint and the peak value of contact force between ball and the track are used as evaluation indexes for characterizing dynamic performance of the joint. A multibody dynamic model of the joint is established to calculate its dynamic performance. In the model, the contact properties and friction characteristics of the internal structures were modeled, and a nonlinear equivalent spring and damping model was adopted for estimating the contact force. The transmission efficiency loss of the cross groove joint was measured and compared with the calculated values.