Table of Content

25 April 2024, Volume 46 Issue 4 Previous Issue   

Select

A Criticality Assessment Model for the Intelligent Vehicle Test Scenario Based on the Onboard Camera Images Bing Zhu,Yinzi Huang,Jian Zhao,Peixing Zhang,Jingwei Xue 2024, 46 (4):  557-563.  doi: 10.19562/j.chinasae.qcgc.2024.04.001 Abstract ( 71 )   HTML ( 4 )   PDF (4680KB) ( 47 )   Save Onboard camera images are the main data sources for constructing the intelligent vehicle test scenario library， but the probability of critical test scenarios occurring in the actual collected onboard camera images is very low， and most of the scenarios have little test value. If it is directly applied to the intelligent vehicle test， it will waste a lot of test resources. In this paper， a criticality assessment model for the intelligent vehicle test scenario based on the onboard camera images is proposed. Firstly， the images collected from real vehicles are processed based on the camera parameters to output parameters that have impact on driving safety. Then， the parameters are integrated using the risk field theory to output the criticality assessment results of the intelligent vehicle test scenario. Finally， the criticality assessment validation is conducted on the images collected from the actual vehicle. The results show that the proposed model can accurately output the specific values of the criticality of the test scenarios in order to compare the test values of different scenarios， proving that the model proposed in this paper can effectively screen the intelligent vehicle critical test scenarios. Figures and Tables | References | Related Articles | Metrics

Select

Research on Automatic Driving Motion Control Based on Double Estimator Reinforcement Learning Combined with Forward Predictive Control Guodong Du,Yuan Zou,Xudong Zhang,Wenjing Sun,Wei Sun 2024, 46 (4):  564-576.  doi: 10.19562/j.chinasae.qcgc.2024.04.002 Abstract ( 33 )   HTML ( 2 )   PDF (8372KB) ( 25 )   Save Motion control research is an important part to achieve the goal of autonomous driving. To solve the problem of suboptimal control sequence due to the limitation of single-step decision in traditional reinforcement learning algorithm， a motion control framework based on the combination of double estimator reinforcement learning algorithm and forward predictive control method （DEQL-FPC） is proposed. In this framework， double estimators are introduced to solve the problem of action overestimation of traditional reinforcement learning methods and improve the speed of optimization. The forward predictive multi-step decision making method is designed to replace the single step decision making of traditional reinforcement learning so as to effectively improve the performance of global control strategies. Through virtual driving environment simulation， the superiority of the control framework applied in path tracking and safe obstacle avoidance of autonomous vehicles is proved， and the accuracy， safety， rapidity and comfort of motion control are guaranteed. Figures and Tables | References | Related Articles | Metrics

Select

Evaluation Method for the Penetration Rate of Perception System Triggering Conditions Junyi Chen,Zhenyuan Liu,Xuezhu Yang,Tianchen Wang,Haixia Li,Tong Jia,Xingyu Xing,Xinzheng Wu 2024, 46 (4):  577-587.  doi: 10.19562/j.chinasae.qcgc.2024.04.003 Abstract ( 22 )   HTML ( 2 )   PDF (3076KB) ( 27 )   Save The issue of safety of the intended functionality （SOTIF） restricts the application of autonomous vehicles. The various extreme driving environments faced by the perception system of autonomous vehicles are highly susceptible to SOTIF problems. Therefore， it is necessary to identify and evaluate a large number of triggering conditions in the safety analysis phase according to the existing SOTIF standards to select high-value trigger conditions so as to provide test scenarios for subsequent test and validation. Firstly， a set of three-dimensional evaluation system for triggering conditions of the perception system including exposure rate， penetration rate and hazard rate is proposed， based on the analysis of the risk evolution process of triggering conditions in autonomous driving system. Subsequently， based on the analytic hierarchy process （AHP）， a quantitative evaluation method for the penetration of triggering conditions is constructed. Finally， 15 triggering conditions for a mass-produced vehicle fusion perception system are selected and analyzed. Test cases are built and the closed site tests are conducted to evaluate the penetration rate of the above triggering conditions. Finally， through the calculation， 3 high-risk triggering conditions are screened， which verifies the feasibility of the quantitative evaluation method of trigger condition penetration rate. Figures and Tables | References | Related Articles | Metrics

Select

Energy-Saving Planning Method for Autonomous Driving Mining Trucks Based on Composite Dynamic Sampling Ting Chikit,Yafei Wang,Yichen Zhang,Mingyu Wu,Yile Wang 2024, 46 (4):  588-595.  doi: 10.19562/j.chinasae.qcgc.2024.04.004 Abstract ( 28 )   HTML ( 1 )   PDF (2688KB) ( 15 )   Save In recent years， path planning methods of autonomous driving mining truck that consider safety and efficiency have been gradually maturing and have been implemented in various mining scenarios. Simultaneously， the utilization of path planning methods to improve the fuel efficiency of mining trucks is paid more and more attention to by both the industry and academia. In response to this requirement， a method for energy-efficient path planning of autonomous driving trucks within mining environments is proposed in this paper. Its primary features encompass the utilization of composite dynamic sampling for S-L （Station - Lateral deviation） and S-T （Station - Time）， based on speed， road gradient， and obstacles. A fuel consumption index for typical terrain scenarios in mining environments is established. Additionally， a comprehensive path evaluation model of safety， efficiency and energy consumption is introduced. To prevent the entrapment of the evaluation model's weights in local optima， an adaptive optimization method based on the particle swarm algorithm with simulated annealing strategy is designed. Through testing in real mining scenarios， the method proposed in this paper has exhibited an average improvement of 11.28% in fuel economy metrics compared to existing methods. Figures and Tables | References | Related Articles | Metrics

Select

Human-Like Driving Control Based on Inverse Model Predictive Control Hui Liu,Fawang Zhang,Shida Nie,Jingliang Duan,Congshuai Guo,Lingxiong Guo 2024, 46 (4):  596-604.  doi: 10.19562/j.chinasae.qcgc.2024.04.005 Abstract ( 22 )   HTML ( 1 )   PDF (2576KB) ( 12 )   Save In this paper， a human-like driving control based on inverse model predictive control is proposed， which realizes human-like driving by updating the weight coefficients of the cost function of the control module using the loss function of the real-time trajectory generated by the model predictive control and the driver's trajectory. The human-like driving control is constructed as a two-layer optimization problem. In the lower layer， real-time state trajectories are generated by solving a typical optimal control problem using model predictive control. The optimization objective function of the lower layer is then updated by minimizing the error between the generated real-time trajectories and those of human drivers in the upper layer. The auxiliary systems based on the differential Pontryagin's Maximum Principle are constructed to solve the gradient of the weight coefficients of the cost function for the real axis trajectory. The driver's driving data are collected from the real vehicle， imitated， and tested. The results show that the method proposed in this paper， compared with two types of inverse optimal control methods based on the virtual-time trajectory， reduces the maximum error with the real trajectory by 73.52% and 65.03% in the three test conditions， with the driving behavior more anthropomorphic and has the generalization performance. Figures and Tables | References | Related Articles | Metrics

Select

Estimation of Road Side Slope Based on Fusion of Extended Kalman Filter and Machine Vision Yunbing Yan,Minghao Yue,Haiwei Li 2024, 46 (4):  605-616.  doi: 10.19562/j.chinasae.qcgc.2024.04.006 Abstract ( 18 )   HTML ( 0 )   PDF (7188KB) ( 14 )   Save To address the difficulty of accurately estimating the lateral slope of the road ahead using existing algorithms， a road lateral slope estimation method based on the fusion of Extended Kalman Filter （EKF） and Machine Vision Based （VB） is proposed. Firstly， a two-degree of freedom model for vehicles with lateral slope is established， and the superposition state of lateral slope and vehicle roll angle is estimated through EKF， with the vehicle roll angle decoupled by multiplying the lateral acceleration with an appropriate gain to obtain the estimated lateral slope of the EKF road. Secondly， based on the principle of visual imaging， the geometric relationship between the lateral slope of roads in two-dimensional images and the relevant parameters in the images is analyzed to obtain an estimated value of the lateral slope of roads in VB. Finally， the final estimation of road lateral slope is obtained through data fusion， making the estimation results redundant and complementary. The simulation and actual vehicle test results show that the fusion algorithm can be applied to slopes with varying lateral slopes and significantly improve the estimation accuracy. Figures and Tables | References | Related Articles | Metrics

Select

Real-Time Pavement Recognition Technology Based on Intelligent Tire System Weidong Liu,Zongzhi Han,Zhenhai Gao,Yanhu Kang 2024, 46 (4):  617-625.  doi: 10.19562/j.chinasae.qcgc.2024.04.007 Abstract ( 21 )   HTML ( 0 )   PDF (4402KB) ( 28 )   Save Under complex and extreme conditions， road adhesion coefficient is an important state parameter for tire force analysis and vehicle dynamics control. Compared with the method of model estimation， the intelligent tire technology can feed back the interaction information between the tire and the road surface to the vehicle control system. In this paper， a method of obtaining road adhesion coefficient of vehicle by combining intelligent tire system and machine learning is proposed. Firstly， considering the driving conditions， the sensor selection is carried out， and the intelligent tire hardware acquisition system based on MEMS three-axis acceleration sensor is developed， and the wireless transmission mode with simplified hardware structure is adopted. Secondly， the data set of machine learning training is collected by vehicle experiments by collecting real car test data on different road surfaces and the wheel-ground relationship and signal characteristics are analyzed. Finally， the feature learning of acceleration timing signal is realized by combining the advantages of CNN and LSTM. The effectiveness and accuracy of the proposed CNN-LSTM dual channel fusion neural network model are verified by comparing with the training results of other neural network models. The road identification scheme proposed in this paper realizes the goal of real-time road recognition， and the hardware and software architecture and neural network model are more suitable for vehicle system loading， providing real-time and accurate road information for vehicle motion control. Figures and Tables | References | Related Articles | Metrics

Select

Combustion and Emission Characteristics of an Ammonia-Biofuel Dual-Fuel Engine Kaiyuan Cai,Wei Wang,Ziqing Zhao,Xiao Ma,Yunliang Qi,Li Li,Zhi Wang 2024, 46 (4):  626-633.  doi: 10.19562/j.chinasae.qcgc.2024.04.008 Abstract ( 16 )   HTML ( 0 )   PDF (2325KB) ( 20 )   Save The application of low-carbon/zero carbon fuels is an effective way to achieve high efficiency and clean combustion of internal combustion engines in response to the goal of ‘carbon-peak and carbon- neutrality’. The study is based on a dual-fuel diesel engine bench. Diesel， blends of biodiesel and gasoline （BG70） and blends of biodiesel， gasoline and ethanol （BG50E20） are chosen as the direct injection fuels. Ammonia is injected into the intake port and the ammonia energy replacement （AER） is 0~28%. The engine speed is 1 200 r/min and the indicated mean effective pressure （IMEP） is 0.8 MPa. The CO， THC， NOx and particle number distributions are compared and analyzed. The results shows that the BG70 and BG50E20 show higher indicated efficiency than that of diesel under single-fuel mode. BG70 reduces CO emission by 30% compared to diesel. However， the THC and NOx emissions are the highest. The total particle number （TPN） emissions of BG70 and BG50E20 are lower than that of diesel. Compared to the combustion and emission characteristics of each fuel under single-fuel mode， adding ammonia leads to a 1%~2% decreasing of the indicated efficiency and doubles the CO emission. The NOx emissions of diesel and BG70 are reduced by nearly 50%， while the reduction of BG50E20 is about 30%. Also， ammonia shows significant effect on the TPN emissions of BG70 and BG50E20. The TPN emission of BG70 increases by 20 times when the AER grows from 0 to 28%. Figures and Tables | References | Related Articles | Metrics

Select

Prediction of the Remaining Useful Life of Real Vehicle Lithium Batteries by Fusion of K-means Clustering and Sequence Decomposition Hongyi Liang,Jikai Chen,Wanli Liu,Fengchong Lan,Bingda Mo,Jiqing Chen 2024, 46 (4):  634-642.  doi: 10.19562/j.chinasae.qcgc.2024.04.009 Abstract ( 23 )   HTML ( 0 )   PDF (2250KB) ( 15 )   Save Influenced by the usage conditions， the state of health （SOH） declining process of lithium-ion power battery of electric vehicles has a lot of fluctuations， which leads to the decrease of model prediction accuracy. In the short-term prediction of the remaining useful life （RUL） of lithium-ion batteries， the SOH fluctuations cannot be ignored， and in order to accurately predict the SOH fluctuations in the short term， effective health indicators need to be extracted from the lithium-ion battery operation data transmitted from real vehicles. A joint distribution feature input and sequence decomposition fusion method for lithium-ion battery RUL prediction is established， using K-means clustering method to construct joint distribution features of vehicle lithium-ion battery operation process， and using S-G filter for sequence decomposition of SOH decline curve. Long-short term memory neural network （LSTM） and multilayer perceptron （MLP） is used respectively for trend part and fluctuation part. The final prediction results are obtained by fusion. The theoretical analysis and the validation of the real vehicle collection data show that the fusion model can predict the short-term decline trend of the vehicle lithium-ion battery RUL while predicting the fluctuation of SOH， and has a high short-term prediction accuracy. Figures and Tables | References | Related Articles | Metrics

Select

Fast Clustering of Retired Lithium-ion Batteries Based on Adaptive Fuzzy C-means Algorithm Lin Chen,Manping He,Shuxiao Wu,Deqian Chen,Mingsi Zhao,Haihong Pan 2024, 46 (4):  643-651.  doi: 10.19562/j.chinasae.qcgc.2024.04.010 Abstract ( 20 )   HTML ( 3 )   PDF (3231KB) ( 11 )   Save The treatment of retired lithium-ion batteries （LiBs） by echelon utilization has great economic and environmental values， and how to sort and reconstitute decommissioned batteries retired LiBs efficiently and accurately is a prominent technical challenge in stepwise utilization. Firstly， to accurately reflect the consistency of retired batteries LiBs， the three factors of maximum available capacity （MAC）， discharge ohmic internal resistance （DOIR） and Frechet distance （FD） of incremental capacity curve， are extracted together as clustering factors. Then the three clustering factors are combined with the adaptive fuzzy C-mean （AFCM） algorithm to construct a clustering method for retired batteries LiBs. The results show that the maximum error of MAC within the clustered clusters of the AFCM algorithm is 79 mAh with the DOIR less than 45 mΩ. The clustering method of the three factors into groups of batteries has better consistency； and the AFCM algorithm clustering takes the shortest time when 117 batteries are clustered. Figures and Tables | References | Related Articles | Metrics

Select

Study on Accurate Modeling and Efficient Simulation of Thermal Runaway Propagation of Battery Modules Nannan Kuang,Botao Hu,Guo Li,Guanglei Zhao,Shuang Feng,Likun Xu 2024, 46 (4):  652-661.  doi: 10.19562/j.chinasae.qcgc.2024.04.011 Abstract ( 21 )   HTML ( 0 )   PDF (5168KB) ( 16 )   Save The simulation of thermal runaway propagation in battery systems is an important step in the development process of battery systems， the results of which can provide guidance and suggestions for the optimization of battery system safety design. Therefore， it is necessary to simplify the thermal runaway propagation model reasonably in order to significantly improve research and development efficiency while meeting the accuracy of the system model. Based on the traditional thermal runaway test and numerical simulation results of the cell， by adopting the research approach of "cell - module”， a simplified thermal runaway propagation model for battery modules with the normalized heat generation equation as the core is constructed in this paper to study the accuracy and computational efficiency of the model. The results show that the computational time of the simplified thermal runaway propagation model for battery modules is 37 minutes， while the computational time of the traditional module model is about 90 minutes under the same conditions. With the accuracy of the models reaching 90%， the computational time is shortened by about 2/3， significantly reducing the computational cost. The research in this paper provides technical reference for efficient and fast simulation of thermal propagation at the battery pack level. Figures and Tables | References | Related Articles | Metrics

Select

Performance Analysis of Elastocaloric Cooler Driven by Waste Heat from Fuel Cell Yuxiang Peng,Qinghua Yu,Rui Ao,Fuwu Yan 2024, 46 (4):  662-668.  doi: 10.19562/j.chinasae.qcgc.2024.04.012 Abstract ( 12 )   HTML ( 0 )   PDF (2061KB) ( 11 )   Save Heat-driven elastocaloric cooling is a new solid-state refrigeration technology which uses shape memory alloy deformation after heating to drive the phase transformation of elastocaloric materials to produce a refrigeration effect. In this paper， a combined system of elastocaloric refrigeration device and fuel cell is designed， which uses the waste heat from fuel cell to drive the elastocaloric-cooling device to improve the efficiency of energy utilization， and produce a cooling effect. Based on the working principles of fuel cell and elastocaloric cooling， a dynamic coupled simulation model for the whole system is established by Simulink software to study the dynamic operation characteristics of the combined system and the influence of operation parameters on the system performance. The results show that adding an elastocaloric cooler can improve the energy utilization efficiency of the whole system. When the operation temperature of fuel cell is 80 ℃， the system can produce 1.76 kW cooling power. Adjusting the operation pressure of fuel cell to 2.5 atm can maximize the comprehensive output power and operation efficiency of the system. The current density of fuel cell has opposite influence on the output power and operation efficiency of the combined system. Figures and Tables | References | Related Articles | Metrics

Select

Continuous Transient Impact Mechanism and Objective Evaluation in Mode Transition for Power Split Hybrid Power System Haodi Li,Zhiguo Zhao,Peng Tang,Yongping Hou 2024, 46 (4):  669-681.  doi: 10.19562/j.chinasae.qcgc.2024.04.013 Abstract ( 19 )   HTML ( 1 )   PDF (9800KB) ( 34 )   Save For the problem that the continuous transient impact mechanism in mode transition process for power split hybrid power system is not clear and there is no objective evaluation index and method， the mechanism of continuous transient impact in mode transition and the objective evaluation method of its performance are explored in this paper. Firstly， a transient torsional vibration model of dedicated hybrid transmission （DHT） is established with consideration of torque crossing zero， time-varying stiffness of gear pair meshing， backlash and torque ripple of drive motor based on the high dynamic performance test bench of the power transmission system. Secondly， the mechanism of torque crossing zero change during DHT mode transition is studied， and a time-domain evaluation index of continuous transient impact performance is proposed considering the number of torque crossing zero and mode transition time， and a frequency-domain evaluation index based on time-frequency characteristics of impact degree is established by short-time Fourier transform method. Finally， taking the transition process of pure electric to power split hybrid mode as an example， the coefficient of variation method is used to objectively analyze and evaluate the continuous transient impact performance under the influence of different accelerator pedal opening and backlash. The results show that the frequent torque crossing zero at the power output end of the power split DHT and the coupling between the backlash are the main causes of the continuous transient impact during the mode transition process. Decreasing the backlash can reduce the continuous transient impact in the maximum amplitude frequency range of 100 ~ 200 Hz. Moreover， according to the comparison of comprehensive evaluation scores， the optimization parameters of continuous transient impact performance during mode transition are obtained. The study can provide support for the optimization of coordinated control strategy for mode transition smoothness of power split hybrid power system. Figures and Tables | References | Related Articles | Metrics

Select

Research on the Adaptability of CO2 Ejector Under Automotive Conditions Ranging from -30 to 50 ℃ Tianyang Yang,Huiming Zou,Hui Zhou,Chunlei Wang,Changqing Tian 2024, 46 (4):  682-690.  doi: 10.19562/j.chinasae.qcgc.2024.04.014 Abstract ( 11 )   HTML ( 0 )   PDF (2937KB) ( 14 )   Save To promote the application of ejector expansion work recovery technology， the research on the comprehensive performance of cooling and heating of the compression-ejection CO2 heat pump system under the operating conditions of the wide temperature range of -30~50 ℃ and the expansion work recovery characteristics of the ejector is carried out in this paper. The focus is placed on analysis of the effect of the working nozzle on the adaptability of the fixed ejector to variable working conditions. The results show that under the cooling conditions， the injection coefficient decreases while the pressure lift ratio increases with the increase of the ambient temperature. Under the heating condition， with the decrease of the ambient temperature， both the injection coefficient ratio and pressure lift ratio increase first and then decrease. Under the cooling conditions， the recovered expansion work of the ejector accounts for 16.7%-37.2% of the maximum recoverable expansion work， while under the heating conditions， it accounts for 9.9%-41.3%. However， the fixed ejector designed for high-temperature cooling conditions is difficult to adapt to low-temperature heating conditions. When deviating from the design conditions， over-expansion at the nozzle outlet will result in shock wave energy loss， while under-expansion at the nozzle outlet in low-temperature heating conditions will cause the ejector to lose its entrainment effect. Figures and Tables | References | Related Articles | Metrics

Select

Study on Automatic Parking Motion Planning Method for Tractor-Semitrailer Based on SMPA Yuanmin Wang,Yafei Wang,Wengang Qin,Hao Chen,Yinhua Liu 2024, 46 (4):  691-702.  doi: 10.19562/j.chinasae.qcgc.2024.04.015 Abstract ( 17 )   HTML ( 1 )   PDF (4699KB) ( 18 )   Save The unstable kinematic characteristics of tractor-semitrailer vehicles bring considerable challenges for autonomous motion planning in the parking process. In this paper， sequential motion planning algorithm （SMPA） is proposed to address the problems of low efficiency of parking motion planning algorithms and poor smoothness of results for tractor-semitrailer in static scenario with multiple obstacles. Firstly， an initial path generation method based on the quadratic planning strategy and improved bidirectional rapidly-exploring random tree algorithm （Bi-RRT） is proposed. Then， combined with the study of path node feasibility discrimination method under vehicle non-complete differential constraints， a probability-based target bias sampling strategy is proposed to improve the sampling efficiency. Finally， a nonlinear optimal control model oriented to the continuity of the control variables of the vehicle system is constructed to solve the docking problem of the parking reversal point and improve the parking trajectory smoothness. Simulation results show that this method reduces the planning time by 86.71% and 21.44% compared to Hybrid A* and Bi-RRT， respectively， in multi obstacle scenarios， with more superior trajectory quality. Figures and Tables | References | Related Articles | Metrics

Select

Fault Diagnosis of Automotive PMSMs Based on EMD-SDP Image Features and Improved DenseNet Jianping Wang,Jian Ma,Dean Meng,Xuan Zhao,Qi Bian,Kai Zhang,Qiquan Liu 2024, 46 (4):  703-716.  doi: 10.19562/j.chinasae.qcgc.2024.04.016 Abstract ( 17 )   HTML ( 0 )   PDF (9404KB) ( 7 )   Save Permanent Magnet Synchronous Motor （PMSM） is widely used in electric vehicle drive systems due to its wide speed range， large output torque， fast speed response， small size and lightweight. PMSMs are susceptible to inter turn short-circuit faults， demagnetization faults， bearing wear faults and other faults due to harsh climate， abnormal vibration and frequent start-brake conditions. In this paper， to address the problems of difficulty in distinguishing PMSM similar faults with single-dimension signals and poor robustness of traditional diagnostic methods when the operating conditions change， a fault diagnosis method is proposed for automotive PMSMs based on the combination of Empirical Mode Decomposition-Symmetric Dot Pattern （EMD-SDP） image features and improved DenseNet convolutional neural network. Firstly， the vibration signals of PMSMs in different states under multiple operating conditions are experimentally obtained， and the pre-processed vibration signals are subjected to EMD to solve the Intrinsic Mode Function （IMF） at different levels. Secondly， the original vibration signals are transformed to the SDP images， and the IMFs at different levels are transformed into RGB color features to be displayed on the SDP images. Then， a classification network model is constructed by improving DenseNet learning image dataset through the fusion of scSE attention mechanism. Finally， the motor state to be measured is evaluated and diagnosed through the signal-image-network process. The diagnostic results show that the proposed method performs well under both steady state and variable speed transient conditions. Under constant speed and load conditions， the proposed method achieves the highest fault diagnosis accuracy （99.72%）， which is 1.66% higher than the accuracy of the DenseNet （98.06%）. The ROC curves of the improved DenseNet model and the DenseNet model are closest to the upper left corner， with the mean AUC values of 0.997 4 and 0.974 5. Under acceleration and deceleration with constant load conditions， the improved DenseNet model also achieves the highest diagnostic accuracies of 96.88% and 97.08%， with the mean AUC values of 0.987 7 and 0.986 9. The overall performance of the proposed method is better than conventional methods and can be effectively used for fault diagnosis during speed changes. Figures and Tables | References | Related Articles | Metrics

Select

Deterioration Experiments of Antifriction and Anti-wear Properties of Vehicle Engine Oils Meng Li,Chaolin Peng 2024, 46 (4):  717-724.  doi: 10.19562/j.chinasae.qcgc.2024.04.017 Abstract ( 22 )   HTML ( 0 )   PDF (5556KB) ( 11 )   Save Antifriction and anti-wear performance is the key to determine the oil change cycle of vehicle engine oils. Based on driving tests and sample collection of engine oils， the evolution rules and degradation mechanisms of antifriction and anti-wear performance of vehicle engine oils are studied by test methods. Firstly， the collected oil samples are tested according to the standard requirements of oil changing， and it is found that the engine oils in the running test is within the oil change index limit when reaching the oil change cycle. Then， the tribological performance tests of the oil samples are carried out using SRV micro motion friction and wear testing machine and four-ball friction and wear testing machine. The results show that there is a service range or time with the best tribological performance during the service life cycle of the engine oil， where the friction coefficient is the smallest and the wear volume is the least. Finally， wear track is characterized and analyzed using scanning electron microscopy and EDAX energy spectrometer， and from the perspective of the change of kinetic viscosity of engine oil， the internal mechanism of the deterioration of antifriction and anti-wear performance of engine oil is analyzed. The thermal decomposition， shear fracture and thermal polymerization of base oil molecules are the keys to the change of friction coefficient of engine oil. The main reason for the change of anti-wear properties is the concentration of extreme pressure anti-wear additives and frictional chemical reaction. The research conclusion of this paper has certain theoretical significance and engineering application value for engine oil development and oil change cycle determination. Figures and Tables | References | Related Articles | Metrics

Select

Gear Rattle Dynamic Analysis Considering Manufacturing Error and Oil Film Force Xiaohui Shi,Yi Zhou,Huihui Xu,Dong Guo,Ziyuan Mei,Xuefang Cui 2024, 46 (4):  725-734.  doi: 10.19562/j.chinasae.qcgc.2024.04.018 Abstract ( 17 )   HTML ( 0 )   PDF (4741KB) ( 27 )   Save In order to accurately evaluate the rattle performance of the gearbox unloaded gear under angular acceleration excitation， a ten-degree-of-freedom torsional dynamic model for the dual-clutch transmission rattle bench test is established using the lumped parameter method. Based on consideration of the contact time varying mesh stiffness， backlash nonlinearities and drag torque， the gear manufacturing error and the oil film induced-force are further considered. Bench test results show that the oil film induced-force model containing oil film entraining and squeeze effects can correctly simulate the transmission rattle response. Numerical analysis results show that lubricant film can reduce the mesh impact induced by the error-induced displacement excitation and time-varying mesh stiffness parametric excitation. The gear rattle intensity increases with the increase of manufacturing error amplitude， and when the error amplitude increases to a certain value， the gear pair produces rattle phenomenon with null angular acceleration as well. The influence of oil temperature on gear rattle is related to the angular acceleration excitation. When there is no angular acceleration excitation， the low-frequency circumferential error with low-amplitude at low-temperature changes the direction of the oil film force and hence produce gear rattle. The rattle response frequency caused by oil film hydrodynamic force is lower than that generated by the structural tooth contact. The rattle intensity increases as the temperature increases in a convex function once the angular acceleration excitation is applied at transmission input shaft. Figures and Tables | References | Related Articles | Metrics

Select

Lightweight Design of Material-Structure for Steel-Aluminum Hybrid Cab Chao Wang,Ming Li,Aiguo Cheng,Zhicheng He,Wanyuan Yu 2024, 46 (4):  735-744.  doi: 10.19562/j.chinasae.qcgc.2024.04.019 Abstract ( 30 )   HTML ( 3 )   PDF (4174KB) ( 15 )   Save In order to obtain a more comprehensive lightweight design for the commercial vehicle cab， a holistic material-structure lightweight method for the steel-aluminum hybrid cab is proposed. Firstly， a performance-driven material selection method is established， which is based on sensitivity analysis， equal stiffness approximation theory， and equal strength theory. The scheme of steel-aluminum hybrid materials for steel cab is preliminary designed. Secondly， the key force transfer paths of the cab are identified by compromise programming method topology optimization and the relevant structures are strengthened. Then， the radial basis function （RBF） surrogate models of cab mass， stiffness， and modal performance are established， by considering the design parameters of the thicknesses of cab parts and cross-section sizes. And the multi-objective particle swarm optimization approach （MOPSO） is used for the multi-objective optimal design of the cab. The optimization results show that the mass of the cab is reduced by 12.8% under the requirements of cab performances of stiffness， modal， and collision. This method has practical engineering guidance value for the lightweight of steel-aluminum hybrid cab. Figures and Tables | References | Related Articles | Metrics