Pub Date : 2024-05-02DOI: 10.1177/09544070241247239
Mirco Bartolozzi, Matteo Massaro, Matteo Mottola, Giovanni Savino
Tyre-road interaction governs motorcycle dynamics; however, the most widespread tyre model formulations must be characterised through a dedicated test bench on the lab or road, unavailable to many interested subjects. This article proposed a new tyre model formulation, conceived to be characterised through riding data using standard instrumentation. Albeit its coefficients are identified through quasi-static, uncombined slip manoeuvres, the model addresses transient, combined manoeuvres and is adaptive to road friction levels and static weight through statistical relationships from the literature. A pre-existing formulation was improved and expanded. The model’s behaviour in demanding conditions was investigated through a high-fidelity simulation environment, using a Magic Formula tyre model as the reference. Next, the characterisation procedure was carried out using actual riding data. The model’s accuracy is shown by reproducing numerically one of the manoeuvres and through comparison with the results of a bench test. The proposed model could correctly reproduce the primary behaviour of a Magic Formula model, also concerning tyre moments and steering torque. Characterising the tyre model through real riding data proved feasible, and its robust formulation limited the propagation of estimation errors. The proposed tyre model formulation and characterisation procedure should interest, among others, those subjects that lack access to a tyre testing machine.
{"title":"An enhanced motorcycle tyre model characterised through experimental riding data","authors":"Mirco Bartolozzi, Matteo Massaro, Matteo Mottola, Giovanni Savino","doi":"10.1177/09544070241247239","DOIUrl":"https://doi.org/10.1177/09544070241247239","url":null,"abstract":"Tyre-road interaction governs motorcycle dynamics; however, the most widespread tyre model formulations must be characterised through a dedicated test bench on the lab or road, unavailable to many interested subjects. This article proposed a new tyre model formulation, conceived to be characterised through riding data using standard instrumentation. Albeit its coefficients are identified through quasi-static, uncombined slip manoeuvres, the model addresses transient, combined manoeuvres and is adaptive to road friction levels and static weight through statistical relationships from the literature. A pre-existing formulation was improved and expanded. The model’s behaviour in demanding conditions was investigated through a high-fidelity simulation environment, using a Magic Formula tyre model as the reference. Next, the characterisation procedure was carried out using actual riding data. The model’s accuracy is shown by reproducing numerically one of the manoeuvres and through comparison with the results of a bench test. The proposed model could correctly reproduce the primary behaviour of a Magic Formula model, also concerning tyre moments and steering torque. Characterising the tyre model through real riding data proved feasible, and its robust formulation limited the propagation of estimation errors. The proposed tyre model formulation and characterisation procedure should interest, among others, those subjects that lack access to a tyre testing machine.","PeriodicalId":54568,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140834235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-02DOI: 10.1177/09544070241246605
Qing Yang, Guangqiang Wu, Shaozhe Zhang
In order to solve the problem that the DCT static shift strategy cannot adapt to the difference in driving style, the driving style identification model based on multi-dimensional data mining and intelligent algorithm heavily depends on vehicle terminal data storage and calculation, an intelligent shift strategy based on “driver-vehicle-cloud” cooperative control is proposed. Firstly, the dynamic model of the DCT vehicle is analyzed, the primary shift schedule is calculated, and a method to adaptively modify the shifting schedule of DCT according to driving style is proposed. Then, many vehicle driving data are collected, cleaned, and reconstructed by wavelet denoising and other methods, and a driving style database with 80-dimensional features is constructed. Five essential features are selected by the ReliefF method, and the driving style recognition model is constructed by combining random forest, support vector machine, naive Bayesian, and other algorithms. Finally, the support vector machine model with the highest precision is selected, and the “driver-vehicle-cloud” collaborative control system is deployed using cloud computing and vehicle-cloud collaborative technology. The experiment car test shows that the system can identify the driver’s driving style in real time and realize the differential shift schedule and driving experience of DCT.
{"title":"Research on DCT shift strategy for various driving style based on “driver-vehicle-cloud” machine learning","authors":"Qing Yang, Guangqiang Wu, Shaozhe Zhang","doi":"10.1177/09544070241246605","DOIUrl":"https://doi.org/10.1177/09544070241246605","url":null,"abstract":"In order to solve the problem that the DCT static shift strategy cannot adapt to the difference in driving style, the driving style identification model based on multi-dimensional data mining and intelligent algorithm heavily depends on vehicle terminal data storage and calculation, an intelligent shift strategy based on “driver-vehicle-cloud” cooperative control is proposed. Firstly, the dynamic model of the DCT vehicle is analyzed, the primary shift schedule is calculated, and a method to adaptively modify the shifting schedule of DCT according to driving style is proposed. Then, many vehicle driving data are collected, cleaned, and reconstructed by wavelet denoising and other methods, and a driving style database with 80-dimensional features is constructed. Five essential features are selected by the ReliefF method, and the driving style recognition model is constructed by combining random forest, support vector machine, naive Bayesian, and other algorithms. Finally, the support vector machine model with the highest precision is selected, and the “driver-vehicle-cloud” collaborative control system is deployed using cloud computing and vehicle-cloud collaborative technology. The experiment car test shows that the system can identify the driver’s driving style in real time and realize the differential shift schedule and driving experience of DCT.","PeriodicalId":54568,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140834243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Intelligent driving has been prevailing worldwide and is also challenging, which can be complicated by the factors of human drivers. In this paper, a novel collision avoidance strategy is proposed to enhance driving safety in highway overtaking by comprehensively considering the driver’s steering intent. First, in order to capture the driver’s operational characteristics from the driving data, we formulate the prediction of the driver’s steering intent and the ego vehicle’s states as a multivariate time series (MTS) forecasting problem, which is then handled by deep learning with a time pattern attention mechanism (DL-Attn). Second, a predictive risk field (PRF) model is proposed to quantify the real-time overtaking risk based on the above prediction results. Then, the overtaking is evaluated via a personalized risk threshold which can be set for a specific driver via experiments. Next, a linear time-varying model predictive control (LTV-MPC) -based assistance controller is designed so as to interfere in the risky overtaking and take over the ego vehicle from the driver to avoid possible collisions. And the feasibility and stability of the closed system are ensured theoretically. Finally, experiments are carried out in three typical cases. The results demonstrate that the proposed strategy can not only effectively improve driving safety for highway overtaking, but also identify safe overtaking to avoid unnecessary interference.
{"title":"A novel collision avoidance strategy for highway overtaking considering the driver’s steering intent","authors":"Zijun Zhang, Weihe Liang, Han Zhang, Wanzhong Zhao, Chunyan Wang, Heng Huang","doi":"10.1177/09544070241232137","DOIUrl":"https://doi.org/10.1177/09544070241232137","url":null,"abstract":"Intelligent driving has been prevailing worldwide and is also challenging, which can be complicated by the factors of human drivers. In this paper, a novel collision avoidance strategy is proposed to enhance driving safety in highway overtaking by comprehensively considering the driver’s steering intent. First, in order to capture the driver’s operational characteristics from the driving data, we formulate the prediction of the driver’s steering intent and the ego vehicle’s states as a multivariate time series (MTS) forecasting problem, which is then handled by deep learning with a time pattern attention mechanism (DL-Attn). Second, a predictive risk field (PRF) model is proposed to quantify the real-time overtaking risk based on the above prediction results. Then, the overtaking is evaluated via a personalized risk threshold which can be set for a specific driver via experiments. Next, a linear time-varying model predictive control (LTV-MPC) -based assistance controller is designed so as to interfere in the risky overtaking and take over the ego vehicle from the driver to avoid possible collisions. And the feasibility and stability of the closed system are ensured theoretically. Finally, experiments are carried out in three typical cases. The results demonstrate that the proposed strategy can not only effectively improve driving safety for highway overtaking, but also identify safe overtaking to avoid unnecessary interference.","PeriodicalId":54568,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140834314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-30DOI: 10.1177/09544070241244837
Chen Zhou, Yu-ze Wu, Zheng Wang, Fei-xiang Xu
Aiming at improving the vehicle handling stability and vehicle control performance by conventional H2/H∞ control, this paper investigates the active front steering (AFS) system by proposing a hierarchical extension H2/H∞ control strategy. Considering uncertainties and the vehicle handling stability, a mixed H2/H∞ control method is presented to generate the additional front wheel angle through tracking the desired vehicle yaw rate in the upper level. Furthermore, to enhance the H2/H∞ control effect, the novel extension control is proposed to adjust the H2/H∞ control signal dynamically according to different domains defined by the vehicle states. To track front wheel angle from the upper level, this study puts forward the fractional-order proportional-integral-derivative (FOPID) controller for driving the electro-hydraulic steering actuators. The hardware-in-the-loop experiments are performed to demonstrate the hierarchical control theory. The test results indicate that the proposed hierarchical extension H2/H∞ control strategy improves the vehicle cornering stability well, as well as can make the vehicle have better handling stability than conventional H2/H∞ and sliding mode control.
{"title":"Hierarchical extension H2/H∞ control approach for active front steering system of vehicle","authors":"Chen Zhou, Yu-ze Wu, Zheng Wang, Fei-xiang Xu","doi":"10.1177/09544070241244837","DOIUrl":"https://doi.org/10.1177/09544070241244837","url":null,"abstract":"Aiming at improving the vehicle handling stability and vehicle control performance by conventional H2/H∞ control, this paper investigates the active front steering (AFS) system by proposing a hierarchical extension H2/H∞ control strategy. Considering uncertainties and the vehicle handling stability, a mixed H2/H∞ control method is presented to generate the additional front wheel angle through tracking the desired vehicle yaw rate in the upper level. Furthermore, to enhance the H2/H∞ control effect, the novel extension control is proposed to adjust the H2/H∞ control signal dynamically according to different domains defined by the vehicle states. To track front wheel angle from the upper level, this study puts forward the fractional-order proportional-integral-derivative (FOPID) controller for driving the electro-hydraulic steering actuators. The hardware-in-the-loop experiments are performed to demonstrate the hierarchical control theory. The test results indicate that the proposed hierarchical extension H2/H∞ control strategy improves the vehicle cornering stability well, as well as can make the vehicle have better handling stability than conventional H2/H∞ and sliding mode control.","PeriodicalId":54568,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140834183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-27DOI: 10.1177/09544070241245176
Xiezhao Lin, Jun Xu, Jianchao Yu, Xiaolong Zhang, Yulan Zheng, Su Li, Heng Du
Heavy-duty vehicles with long bodies, a large number of axles and large loads are subject to increasingly high requirements for precise steering technology due to the increasing trend toward energy conservation and intelligent assisted driving as well as variable driving conditions. In this paper, an energy-efficient open circuit variable-speed pump-controlled steering system (OPCEHSSS) adapted for heavy loads is used, but its strong flow output nonlinearity and system nonlinear dynamic behavior greatly impede the steering performance. Therefore, in order to reduce the influence of the flow leakage of the fixed-displacement pump on the system and to ensure that the flow output of the system matches the control model, a mapping model based on the fitting of a two-layer neural network algorithm with a dynamic real-time compensation strategy (FNC) is proposed. In addition, considering the strong robustness of the system even under parameter uncertainty and unknown disturbance, a complex nonlinear mathematical model is established based on OPCEHSSS physical characteristics, and a dual-objective control strategy of steering angle and pressure based on sliding mode control (SMC) is proposed. However, in order to reduce the influence of high-order switching discontinuity on the steering and ensure the fast convergence of the control system, a fast super twisting algorithm (STA) based on double saturation function of the boundary layer is proposed. The experimental results show that the three different controllers can effectively reduce the steering angle error after the introduction of FNC. And in the case of a single axle loaded with 6 tons, the improved new FNC+STA integrated dual-objective control strategy improves the accuracy by 53.16% compared with PID and 40.67% compared with SMC. The steady-state error is maintained within 0.9°, realizing the high-performance steering tracking control of OPCEHSSS for heavy vehicles.
{"title":"High-performance steering tracking control of open circuit variable-speed pump-controlled steering system for heavy-duty vehicles based on flow nonlinearity compensation","authors":"Xiezhao Lin, Jun Xu, Jianchao Yu, Xiaolong Zhang, Yulan Zheng, Su Li, Heng Du","doi":"10.1177/09544070241245176","DOIUrl":"https://doi.org/10.1177/09544070241245176","url":null,"abstract":"Heavy-duty vehicles with long bodies, a large number of axles and large loads are subject to increasingly high requirements for precise steering technology due to the increasing trend toward energy conservation and intelligent assisted driving as well as variable driving conditions. In this paper, an energy-efficient open circuit variable-speed pump-controlled steering system (OPCEHSSS) adapted for heavy loads is used, but its strong flow output nonlinearity and system nonlinear dynamic behavior greatly impede the steering performance. Therefore, in order to reduce the influence of the flow leakage of the fixed-displacement pump on the system and to ensure that the flow output of the system matches the control model, a mapping model based on the fitting of a two-layer neural network algorithm with a dynamic real-time compensation strategy (FNC) is proposed. In addition, considering the strong robustness of the system even under parameter uncertainty and unknown disturbance, a complex nonlinear mathematical model is established based on OPCEHSSS physical characteristics, and a dual-objective control strategy of steering angle and pressure based on sliding mode control (SMC) is proposed. However, in order to reduce the influence of high-order switching discontinuity on the steering and ensure the fast convergence of the control system, a fast super twisting algorithm (STA) based on double saturation function of the boundary layer is proposed. The experimental results show that the three different controllers can effectively reduce the steering angle error after the introduction of FNC. And in the case of a single axle loaded with 6 tons, the improved new FNC+STA integrated dual-objective control strategy improves the accuracy by 53.16% compared with PID and 40.67% compared with SMC. The steady-state error is maintained within 0.9°, realizing the high-performance steering tracking control of OPCEHSSS for heavy vehicles.","PeriodicalId":54568,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140812559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Evaluation of autonomous vehicles is one of the major challenges before they can be released. Due to the advantages in efficiency, cost, and safety, scenario-based simulation methods have recently received great attention. Even so, as the complexity and uncertainty exist in the real driving environment, the scenarios that autonomous vehicles may encounter are infinite. Therefore, it is necessary to classify simulation scenarios according to their criticality. It contributes to accelerating the evaluation processes. This paper presents a novel criticality evaluation method, based on a proposed Digital Expert, for car-following autonomous driving. The Digital Expert acts as the evaluator to evaluate the criticality of scenarios depending on their driving performance. Driving performance refers to the achieved degree of driving intentions. Firstly, a Digital Expert is established as the evaluator for the criticality of the scenario using the inverse reinforcement learning method. Then, based on the fact that the intention of Digital Expert is to maximize its internal reward function, the reward function is used to evaluate driving performance. Finally, calculating the criticality of the car-following scenario according to the mapping relationship between driving performance and criticality. Using the driving data in the NGSIM data set, this paper generates two groups of simulated car-following scenarios and evaluates the criticalities of the two scenarios. The experimental results show that the proposed criticality evaluation method can reasonably evaluate the criticality of car-following scenarios.
{"title":"Evaluation method with digital expert on the criticality of car-following scenarios for autonomous vehicles testing","authors":"Jiangfeng Nan, Weiwen Deng, Rui Zhao, Bowen Zheng, Zhicheng Xiao, Juan Ding","doi":"10.1177/09544070241245484","DOIUrl":"https://doi.org/10.1177/09544070241245484","url":null,"abstract":"Evaluation of autonomous vehicles is one of the major challenges before they can be released. Due to the advantages in efficiency, cost, and safety, scenario-based simulation methods have recently received great attention. Even so, as the complexity and uncertainty exist in the real driving environment, the scenarios that autonomous vehicles may encounter are infinite. Therefore, it is necessary to classify simulation scenarios according to their criticality. It contributes to accelerating the evaluation processes. This paper presents a novel criticality evaluation method, based on a proposed Digital Expert, for car-following autonomous driving. The Digital Expert acts as the evaluator to evaluate the criticality of scenarios depending on their driving performance. Driving performance refers to the achieved degree of driving intentions. Firstly, a Digital Expert is established as the evaluator for the criticality of the scenario using the inverse reinforcement learning method. Then, based on the fact that the intention of Digital Expert is to maximize its internal reward function, the reward function is used to evaluate driving performance. Finally, calculating the criticality of the car-following scenario according to the mapping relationship between driving performance and criticality. Using the driving data in the NGSIM data set, this paper generates two groups of simulated car-following scenarios and evaluates the criticalities of the two scenarios. The experimental results show that the proposed criticality evaluation method can reasonably evaluate the criticality of car-following scenarios.","PeriodicalId":54568,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140812476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-26DOI: 10.1177/09544070241247369
Ran Gong, Fengming Sun, Cheng Wang, He Zhang
The harsh operating conditions of heavy-duty vehicles accelerates the wear of the sealing ring in the transmission, leading to increased oil leakage and a reduction of the operating pressure in the piston cylinder of wet clutch. This impairs the proper functioning of the transmission in the heavy-duty vehicle. Therefore, it is necessary to predict the pressure loss inside the transmission quickly and effectively after the wear of the sealing ring. The wear of the sealing ring under different operating conditions is calculated through the modified Archard model. The relationship between the oil leakage and pressure loss after the wear of the sealing ring is analyzed using Fluent software. The analysis involves the effects of different wear levels of the sealing ring. The simulation results are validated through a high-speed oil cylinder performance test rig. Based on the validated simulation data and test data, a prediction model for pressure loss is established by using stacking ensemble learning with MLR (multiple linear regression), DTR (decision tree regression), and SVR (support vector regression) as the base learners and RF (random forest) as the meta-learner. The risk of model overfitting is reduced through k-fold cross-validation. The research results indicate that the fused stacking ensemble learning algorithm fully utilizes the advantages of each base learner and can effectively predict the pressure loss after the wear of the sealing ring, and achieve a higher accuracy. The establishment of this model provides theoretical support for real-time prediction of pressure loss after the wear of the sealing ring in actual heavy-duty vehicles.
重型车辆恶劣的工作条件加速了变速器密封环的磨损，导致漏油增加和湿式离合器活塞缸工作压力降低。这将影响重型车辆变速器的正常工作。因此，有必要快速有效地预测密封环磨损后变速器内部的压力损失。密封环在不同工作条件下的磨损是通过改进的 Archard 模型计算得出的。使用 Fluent 软件分析了密封环磨损后漏油和压力损失之间的关系。分析涉及密封环不同磨损程度的影响。模拟结果通过高速油缸性能试验台进行了验证。根据验证后的模拟数据和测试数据，以 MLR（多元线性回归）、DTR（决策树回归）和 SVR（支持向量回归）为基础学习器，以 RF（随机森林）为元学习器，通过堆叠集合学习建立了压力损失预测模型。通过 k 倍交叉验证降低了模型过拟合的风险。研究结果表明，融合堆叠集合学习算法充分发挥了各基础学习器的优势，能有效预测密封环磨损后的压力损失，并达到了较高的精度。该模型的建立为实时预测实际重型车辆密封环磨损后的压力损失提供了理论支持。
{"title":"Predictive method of pressure loss in wet clutch caused by seal wear based on stacking ensemble learning","authors":"Ran Gong, Fengming Sun, Cheng Wang, He Zhang","doi":"10.1177/09544070241247369","DOIUrl":"https://doi.org/10.1177/09544070241247369","url":null,"abstract":"The harsh operating conditions of heavy-duty vehicles accelerates the wear of the sealing ring in the transmission, leading to increased oil leakage and a reduction of the operating pressure in the piston cylinder of wet clutch. This impairs the proper functioning of the transmission in the heavy-duty vehicle. Therefore, it is necessary to predict the pressure loss inside the transmission quickly and effectively after the wear of the sealing ring. The wear of the sealing ring under different operating conditions is calculated through the modified Archard model. The relationship between the oil leakage and pressure loss after the wear of the sealing ring is analyzed using Fluent software. The analysis involves the effects of different wear levels of the sealing ring. The simulation results are validated through a high-speed oil cylinder performance test rig. Based on the validated simulation data and test data, a prediction model for pressure loss is established by using stacking ensemble learning with MLR (multiple linear regression), DTR (decision tree regression), and SVR (support vector regression) as the base learners and RF (random forest) as the meta-learner. The risk of model overfitting is reduced through k-fold cross-validation. The research results indicate that the fused stacking ensemble learning algorithm fully utilizes the advantages of each base learner and can effectively predict the pressure loss after the wear of the sealing ring, and achieve a higher accuracy. The establishment of this model provides theoretical support for real-time prediction of pressure loss after the wear of the sealing ring in actual heavy-duty vehicles.","PeriodicalId":54568,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140802134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-22DOI: 10.1177/09544070241242827
Akif Yavuz, Osman Taha Sen
As an NVH problem observed in automotive brake systems, the significance of brake squeal phenomenon still persists; and the estimation of squeal propensity is a crucial operation. Thus, developing different means to define squeal propensity is an important task. As being one metric, squeal index is widely used for this purpose; and there already exists several different squeal index formulations in the available literature. The main objective of this study is to develop new squeal index formulations and assess their performances. Though, unlike the existing formulations, not only the measured data but also the operating and design parameters of the system of interest are also incorporated in the proposed formulations. The experimental data used for squeal index formulations are obtained on a mass-sliding belt experiment, where squeal-like behavior can successfully be observed. Experiments are conducted at different operating and design parameter conditions. First, cases with presence and absence of squeal-like behavior are observed on the measured data in time and frequency domains. Then, eight different squeal index formulations are developed, where different set of parameters are utilized in each formulation. Some of these parameters are extracted from the measured data (time and frequency domains), and the rest are adopted from the key operating and design parameters. Finally, the performances of proposed squeal indexes are assessed through their relative amplitude variations. In conclusion, the second squeal index formulation is found to have superior performance for distinguishing the effects of operating and design parameters on the occurrence of squeal-like behavior. Furthermore, the sixth squeal index formulation is found to have the best performance for differentiating the effect of spring stiffness on the initiation of squeal-like behavior.
{"title":"Development and performance assessment of different squeal index metrics based on experimental data","authors":"Akif Yavuz, Osman Taha Sen","doi":"10.1177/09544070241242827","DOIUrl":"https://doi.org/10.1177/09544070241242827","url":null,"abstract":"As an NVH problem observed in automotive brake systems, the significance of brake squeal phenomenon still persists; and the estimation of squeal propensity is a crucial operation. Thus, developing different means to define squeal propensity is an important task. As being one metric, squeal index is widely used for this purpose; and there already exists several different squeal index formulations in the available literature. The main objective of this study is to develop new squeal index formulations and assess their performances. Though, unlike the existing formulations, not only the measured data but also the operating and design parameters of the system of interest are also incorporated in the proposed formulations. The experimental data used for squeal index formulations are obtained on a mass-sliding belt experiment, where squeal-like behavior can successfully be observed. Experiments are conducted at different operating and design parameter conditions. First, cases with presence and absence of squeal-like behavior are observed on the measured data in time and frequency domains. Then, eight different squeal index formulations are developed, where different set of parameters are utilized in each formulation. Some of these parameters are extracted from the measured data (time and frequency domains), and the rest are adopted from the key operating and design parameters. Finally, the performances of proposed squeal indexes are assessed through their relative amplitude variations. In conclusion, the second squeal index formulation is found to have superior performance for distinguishing the effects of operating and design parameters on the occurrence of squeal-like behavior. Furthermore, the sixth squeal index formulation is found to have the best performance for differentiating the effect of spring stiffness on the initiation of squeal-like behavior.","PeriodicalId":54568,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140635922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the uncoupled shared steering architecture based on the steering-by-wire (SBW) system, direct access to road feel and automation-related information is unavailable to the driver. To address this problem, this paper proposes a steering wheel torque feedback model that considers human-machine interaction information. First, the model predictive control (MPC) is adopted in lateral vehicle control by automation. Then a fuzzy control-based control authority allocation model is applied to assign the control authority weight between the human driver and automation according to the value of the Path Lateral Hazard (PLH) Factor and the Driver’s Intent Evaluation (DIE) Factor. These two factors reflect the probability of lateral vehicle collision and the intensity of the driver’s driving intention, respectively. Next, the road feel feedback torque and the human-machine interface (HMI) feedback torque is incorporated in the steering wheel feedback torque model to enhance the driver’s experience in SBW vehicles and trust in the automation. The HMI feedback torque is designed to provide human drivers with information on control authority weight variation and desired angle deviation between the human driver and automation. Simulation and experiment results suggest that the proposed uncouple shared control method can accelerate driver acceptance of automation and provide the driver with a more intuitive steering experience.
{"title":"Design of steering wheel torque with human-machine interaction for uncoupled shared steering","authors":"Chaoning Chen, Hongyu Zheng, Changfu Zong, Chuyo Kaku","doi":"10.1177/09544070241246578","DOIUrl":"https://doi.org/10.1177/09544070241246578","url":null,"abstract":"In the uncoupled shared steering architecture based on the steering-by-wire (SBW) system, direct access to road feel and automation-related information is unavailable to the driver. To address this problem, this paper proposes a steering wheel torque feedback model that considers human-machine interaction information. First, the model predictive control (MPC) is adopted in lateral vehicle control by automation. Then a fuzzy control-based control authority allocation model is applied to assign the control authority weight between the human driver and automation according to the value of the Path Lateral Hazard (PLH) Factor and the Driver’s Intent Evaluation (DIE) Factor. These two factors reflect the probability of lateral vehicle collision and the intensity of the driver’s driving intention, respectively. Next, the road feel feedback torque and the human-machine interface (HMI) feedback torque is incorporated in the steering wheel feedback torque model to enhance the driver’s experience in SBW vehicles and trust in the automation. The HMI feedback torque is designed to provide human drivers with information on control authority weight variation and desired angle deviation between the human driver and automation. Simulation and experiment results suggest that the proposed uncouple shared control method can accelerate driver acceptance of automation and provide the driver with a more intuitive steering experience.","PeriodicalId":54568,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140635925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the research of Automated Guided Vehicle (AGV) scheduling, the most critical issues are the optimization of task allocation to AGVs and the handling of conflict scenarios. To address these challenges, we propose a scheme for AGV scheduling optimization and conflict resolution. To begin with, we introduce a novel improved genetic algorithm grounded on a combination strategy that re-encodes tasks into compound groupings, effectively simplifying large-scale integer programing problems into smaller, more manageable ones. Subsequently, the simplified problem is solved using an improved genetic algorithm. Test results validate that this method not only quickens the pace of finding solutions but also significantly improves the quality of those solutions. This is particularly evident when it comes to managing larger-scale optimization challenges. Furthermore, within AGV system conflict scenarios, this paper divides them into two primary categories: navigational conflicts and task quantity changes. For navigational conflicts, three resolution approaches are designed to address four different types of conflict situations: head-on, crossing, occupation, and chasing conflicts. Considering the fluctuations in task quantity, we developed strategies for rescheduling, non-rescheduling, and insertion rescheduling. Their performances were experimentally compared across various scales of scheduling problems, providing data support and theoretical basis for the selection of scheduling strategies in practical applications.
{"title":"Research on AGV scheduling and potential conflict resolution in port scenarios: based on improved genetic algorithm","authors":"Maoquan Feng, Pengyu Wang, Weihua Wang, Kaixuan Li, Qiyao Chen, Xinyu Lu","doi":"10.1177/09544070241244420","DOIUrl":"https://doi.org/10.1177/09544070241244420","url":null,"abstract":"In the research of Automated Guided Vehicle (AGV) scheduling, the most critical issues are the optimization of task allocation to AGVs and the handling of conflict scenarios. To address these challenges, we propose a scheme for AGV scheduling optimization and conflict resolution. To begin with, we introduce a novel improved genetic algorithm grounded on a combination strategy that re-encodes tasks into compound groupings, effectively simplifying large-scale integer programing problems into smaller, more manageable ones. Subsequently, the simplified problem is solved using an improved genetic algorithm. Test results validate that this method not only quickens the pace of finding solutions but also significantly improves the quality of those solutions. This is particularly evident when it comes to managing larger-scale optimization challenges. Furthermore, within AGV system conflict scenarios, this paper divides them into two primary categories: navigational conflicts and task quantity changes. For navigational conflicts, three resolution approaches are designed to address four different types of conflict situations: head-on, crossing, occupation, and chasing conflicts. Considering the fluctuations in task quantity, we developed strategies for rescheduling, non-rescheduling, and insertion rescheduling. Their performances were experimentally compared across various scales of scheduling problems, providing data support and theoretical basis for the selection of scheduling strategies in practical applications.","PeriodicalId":54568,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140636031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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