Vehicle Model Research Articles

Insights into stability and control of the powerslide motion with variable drive torque distribution – applied to a driver assistance system

In this study, a theoretical investigation of the steady-state powerslide motion, or drift, is conducted to gain insight into the influence of the total drive torque and front/rear axle drive torque distribution on the powerslide dynamics of an all-wheel drive vehicle, including the case of a rear-wheel drive vehicle. The steady-state conditions and stability properties are derived, and different actuator inputs, i.e. steering angle, total drive torque and drive torque distribution, to stabilise the unstable powerslide motion are analysed and discussed with respect to different control strategies. The results indicate that the drive torque distribution is an effective control input for stabilisation and can be superior to the total drive torque input. The powerslide cannot be stabilised for particular conditions with the total drive torque input at fixed drive torque distribution. Based on these findings, a driver assistance system is presented that allows the human driver to track a desired circular path only by steering commands. The powerslide motion is stabilised automatically by a controller acting on the total drive torque and on the drive torque distribution if favourable. The characteristics, limitations in dynamics and reactions of a human driver are considered by introducing a virtual test driver model in a simulation environment. The successfully performed powerslide is shown in simulation with a basic vehicle model and in an experimental setup with a test vehicle.

Micro-Assembly Error Control of Specialized MEMS Friction Sensor

A skin friction sensor is a three-dimensional MEMS sensor specially designed for measuring the skin friction of hypersonic vehicle models. The accuracy of skin friction measurement under hypersonic laminar flow conditions is closely related to the fabrication and micro-assembly accuracy of MEMS skin friction sensors. In order to achieve accurate skin friction measurement, high-precision linear laser scanning ranging, multi-axis precision drive, and 3D reconstruction algorithms are investigated; a MEMS skin friction sensor micro-assembly height error measurement system is developed; and the MEMS skin friction sensor micro-assembly height error control method is carried out. The results show that the micro-assembly height error measurement of MEMS skin friction sensors achieves an accuracy of up to 2 μm. The height errors of the MEMS skin friction sensor were controlled within −8 μm to +10 μm after error control. The angular errors were controlled within the range of 0.05–0.25°, significantly improving micro-assembly accuracy in the height direction of the MEMS skin friction sensor. The results of hypersonic wind tunnel tests indicate that the deviation in the accuracy of the MEMS skin friction sensors after applying height error control is about 5%, and the deviation from the theoretical value is 8.51%, which indicates that height error control lays the foundation for improving the accuracy of skin friction measurement under hypersonic conditions.

Stability analysis of linear single-track models with transient tyre dynamics

The present paper investigates the behaviour of linear single-track vehicle models considering transient tyre dynamics. In particular, the tyre models covered in this paper include lumped-parameter approximations, like the famous single-contact point and two-regime formulations, as well as refined distributed brush-like representations formulated in terms of partial differential equations (PDEs). In the first case, the resulting description consists of a system of linear ordinary differential equations (ODEs), whereas, in the second case, the lateral vehicle dynamics are captured by an interconnection of ODEs and PDEs. Stability analyses are conducted concerning both variants. In particular, for single-track models equipped with lumped tyre approximations, sufficient conditions on the vehicle's parameters that ensure dynamical stability are explicitly derived. Concerning the more involved formulation that considers the exact distributed brush model, the exact criterion for stability is instead deduced analytically, and then a qualitative analysis is performed. Via a number of examples, it is revealed that, for a wide range of frequencies of the steering input, the vehicle's behaviour predicted by the two models is similar to that obtained using the classic single-track formulation. The findings contained in the paper may assist vehicle manufacturers in setting requirements on design parameters, and in developing control algorithms that account for transient tyre dynamics at different degrees of accuracy.

Research on ACC Control Strategy for Electric Vehicles Considering Cut-in Behavior

Background: Current adaptive cruise control systems primarily focus on the main target vehicle in the primary lane. However, there may be a delay in updating their targets after a side vehicle merges. This delay can impede the effective utilization of driving data from merging side vehicles, resulting in delayed responses and an increased risk of collision. Objective: Aiming at the problem of untimely control of traditional adaptive cruise control systems in cut-in scenarios, the authors establish a parallel model and collision analysis and design the control strategy of adaptive cruise under parallel behavior to improve the safety, following, and comfort of ACC. Methods: Initially, the vehicle model provided by CarSim is utilized and the power system parameters are adjusted to match the dynamic performance index. The permanent magnet synchronous motor model is then simplified to create an electric vehicle model. Following this, the cruise control system is designed using a PID control method, incorporating a hierarchical control structure. Simultaneously, a cut-in model characterized by sinusoidal acceleration is established, and a collision avoidance strategy based on cut-in behavior is formulated. Building on this foundation, an adaptive cruise control strategy is developed that takes cut-in behavior into account. Results: A test scenario utilizing the CarSim and Simulink co-simulation platforms was developed to simulate and validate the conditions for mode switching and lane cut-in. The ACC system, considering cut-in behavior recognition, demonstrated an ability to control the lead vehicle approximately 1 second earlier than traditional ACC systems. Notably, the distance error improved from - 0.1039 m to 0.040 m, the absolute value of velocity error decreased from 2.65 m/s to 2.02 m/s, the absolute value of main vehicle acceleration reduced by 34%, and the jerk response in acceleration diminished by approximately 14%. Conclusion: The proposed multi-objective cooperative adaptive cruise control system, which considers cut-in behavior, is effective.

Adaptive terminal sliding mode control of hypersonic vehicles based on ESO

PurposeThis paper aims to solve the uncertainty problem of hypersonic vehicle tracking control; an adaptive terminal sliding mode control (TSMC) method based on extended state observer (ESO) is proposed. The combination of adaptive techniques, TSMC and ESO offers an effective approach for managing uncertain systems.Design/methodology/approachThe dynamic model of a hypersonic vehicle is transformed into two control-oriented subsystems. The control system design incorporates an adaptive technique, an ESO and a TSMC. The ESO estimates the primary uncertainties, while the adaptive technique determines the upper limit of secondary uncertainties. These estimates are used for the design of the TSMC law. In addition, the filter is used to generate the reference trajectory to improve the dynamic performance of the system. The stability of the closed-loop system is proved by the Lyapunov stability theory.FindingsA robust control system for hypersonic vehicles is developed with guaranteed stability and strong adaptability to various uncertainties such as parameter variations, external disturbances and actuator faults. Furthermore, the proposed system demonstrates enhanced dynamic performance compared to observer-based sliding mode control. Specifically, for the velocity and altitude tracking control, the settling time of the proposed sliding mode control is approximately 100 s and 70 s shorter than that of the observer-based sliding mode control, respectively.Originality/valueDifferent from the single equivalent treatment, various uncertainties here are classified and treated with different strategies, which improves the disturbance rejection ability of the control system. This ability is of great significance for enhancing the autonomy, adaptability and reliability of hypersonic vehicles in extreme environments.

Modeling of Tank Vehicle Rollover Risk Assessment on Curved–Slope Combination Sections for Sustainable Transportation Safety

Tank vehicles are highly prone to rollover accidents, especially on curved–slope combination sections, which can cause hazardous chemical spills, endangering the environment, public safety, and human health. Therefore, it is crucial to conduct research aimed at reducing the risk of such incidents. Method: The rollover risk of tank vehicles under various loading conditions while traveling on curved–slope combination sections was investigated using driver–vehicle–road dynamics simulation. A multiple linear regression model was then developed to further quantify the impact of key factors on the rollover risk. Results: The results revealed that the road curve radius, vehicle operating speed, and liquid cargo fill level have the greatest impact on a tank vehicle’s rollover risk, and higher fill levels, higher speeds, and steeper downhill slopes all amplify the impact of curve radius on the rollover risk. In some cases, adhering to the road’s speed limit alone was insufficient to ensure the safe passage of the tank vehicle through curves. Conclusions: This study introduced, for the first time, a rollover risk assessment model for tank vehicles operating on curved–slope combination sections. The findings reveal effective methods to improve the transportation safety of tank vehicles. Practical Applications: The findings of this study can assist transportation agencies in selecting routes with lower rollover risks for tank vehicles with different configurations, as well as guide the development of loading standards and curve speed limits. This will effectively reduce rollover accidents of tank vehicles and support sustainable, safer transportation practices.

Tooth design and verification of face spline transmission in hub bearing

The hub bearing with face spline transmission is a new type of hub bearing. The design method and standards are lack. In this work, the geometric relationship of the tooth profile parameters and the meshing situation are derived for the hub bearing face spline. The outer diameter D, number of teeth Z, and face tooth profile angle φ are key design parameters. The verification conditions for preload, extrusion stress, tooth root bending stress, and shear stress are established based on strength theory and assumption of uniform load distribution. To validate the design, a case study is conducted on the hub bearing face spline in a certain vehicle model, with theoretical calculation and simulation of stress by using Finite Element Method. The present FEM results compare well with those of the literature data. Finally, through the incorporation the torque strength and durability tests of the face spline, the reliability of the design theory in this work is confirmed. The results indicates that, the tooth tip fillet r1 has a significant impact on the meshing area, the maximum principal stress near the tooth root is highest at the arc of the tooth root, proving that the selection of the dangerous section is appropriate. The work has value in promoting the development of automotive wheel hub bearings.

Road preview method for active suspension based on reinforcement learning

Abstract The pre-identification of impulse road surfaces, such as speed bumps, potholes, and manhole covers, can significantly enhance the performance of active suspension systems. However, existing current identification methods often fail to balance between low cost, high reliability, and precise speed. This study proposes an impulse road surface identification method based on a reinforcement learning network. First, a real dataset of impulse road surfaces was collected, with suspension dynamic responses serving as inputs and random road levels along with impulse road surface features as outputs. This data was used to develop a Random Forest Extreme Gradient Boosting (RF-XGBoost) network to recognize road surface information from vehicle dynamics. Next, a semantic segmentation network was employed to segment road surfaces during intelligent vehicle travel, using road surface information identified by the random forest network as the reward function for reinforcement learning. The reinforcement learning policy was pre-trained using the actual collected data. To validate the effectiveness of the proposed control strategy, a simulation environment was constructed in Prescan, where speed bumps, potholes, and manhole covers of varying heights and sizes were randomly arranged. The reinforcement learning-based road surface identification algorithm was implemented in Simulink, and a co-simulation was conducted with the CarSim vehicle model. The enhanced RF-XGBoost network effectively distinguishes between random and impulse road surfaces, achieving average recognition accuracies of 95.7% for random surfaces and 98.2% for impulse surfaces. During initial training iterations, the RL network exhibited lower accuracy; however, as training progressed, the accuracy for unfamiliar road surface features reached an average of 96.5%, and overall recognition accuracy improved by an average of 9%. The simulation results demonstrate that the proposed reinforcement learning identification method effectively acquires information about the road ahead, shows robustness and generalization, and provides crucial disturbance data for subsequent active suspension control.

Holistic Electric Powertrain Component Design for Battery Electric Vehicles in an Early Development Phase

As battery electric vehicles (BEVs) gain significance in the automotive industry, manufacturers must diversify their vehicle portfolios with a wide range of electric vehicle models. Electric powertrains must be designed to meet the unique requirements and boundary conditions of different vehicle concepts to provide satisfying solutions for their customers. During the early development phases, it is crucial to establish an initial powertrain component design that allows the respective divisions to develop their components independently and minimize interdependencies, avoiding time- and cost-intensive iterations. This study presents a holistic electric powertrain component design model, including the high-voltage battery, power electronics, electric machine, and transmission, which is meant to be used as a foundation for further development. This model’s simulation results and performance characteristics are validated against a reference vehicle, which was torn down and tested on a vehicle dynamometer. This tool is applicable for an optimization approach, focusing on achieving optimal energy consumption, which is crucial for the design of battery electric vehicles.

Fault-Tolerant Model Predictive Control for Autonomous Underwater Vehicles Considering Unknown Disturbances

This paper presents a fault-tolerant model predictive control approach for cross-rudder autonomous underwater vehicles to achieve heading control, considering rudder stuck faults and unknown disturbances. Specifically, additive faults in the rudders are addressed, and an active fault-tolerant control strategy is employed. Fault models of autonomous underwater vehicles have been established to develop the fault-tolerant control method. In the controller design, the stuck faults of complete rudder failure are incorporated to ensure the heading angle control of the autonomous underwater vehicle in faulty conditions. Furthermore, the fault term is decoupled from the control input, and the decoupled control input, along with corresponding constraints, is incorporated into the model’s predictive controller design. This approach facilitates controller reconfiguration, thereby enhancing and optimizing control performance. Simulation results demonstrate that the proposed fault-tolerant model predictive control method can effectively achieve stable navigation and heading adjustment under rudder fault conditions in autonomous underwater vehicles.

Height Control Method for Air Suspension Systems Based on Model-Free Adaptive Control and Improved Genetic Algorithm

<div>This article presents a height control method for air suspension systems, which are influenced by strong nonlinearity and multiple coupling factors, based on model-free adaptive control (MFAC) using full-form dynamic linearization (FFDL). To address the impact of different damping coefficients of the shock absorber on the height control effect, an improved genetic algorithm is employed to globally optimize the relevant parameters involved in the design of the control law, thereby enhancing the height control performance. The precision of modeling the air suspension system has a direct impact on the simulation of both static and dynamic vehicle models, as well as the accuracy of height control. In this article, an equivalent thermodynamic model of the air suspension system is established based on the principle of energy conservation for height control research. Considering the nonlinearity of the air suspension system and the need to make additional assumptions before modeling, a MFAC method using FFDL is adopted for controller design. Traditional height control methods do not consider the impact of changes in the shock absorber damping coefficient on the height control effect. For different damping coefficients, the body height tracking error is large when using the same height control law initialization parameters. Therefore, an improved genetic algorithm is employed to globally optimize the MFAC parameters under different damping states. The effectiveness of the thermodynamic model of the air suspension system and the MFAC method for height control, with parameters tuned using the improved genetic algorithm, was validated through MATLAB/Simulink simulations.</div>

Control Synthesis for Hypersonic Vehicle Flight Testing with Input–Output-Sampled Nonlinearities

The flight testing of hypersonic vehicles is challenging due to the nonlinear, uncertain, and possibly unstable dynamics of these vehicles. This paper presents a control synthesis method for a hypersonic vehicle with the purpose of guaranteeing robust stability during flight testing when accurate analytic vehicle models are unavailable. Conventional model-based approaches typically rely on curve fitting of numerical simulation and test data, which can introduce inaccuracies. Our proposed approach assumes knowledge of the linear form of the ordinary differential equation and uses quadratic constraints to bound sampled data that accounts for the nonlinear and uncertain portion of the model for controller synthesis by iteratively solving convex semidefinite programs. A numerical example demonstrating the effectiveness of the proposed method is performed before applying the technique to a nonlinear hypersonic vehicle found in the literature. Using the synthesized stabilizing controller, we demonstrate that the vehicle states remain within a certified bound given a known harmonic excitation when initiated from a quantified set of allowable initial conditions. Based on these simulation results, our proposed control synthesis method shows promise in ensuring robustness when performing hypersonic vehicle flight testing.

A Cross-Platform Mobile Application to Display the Important Features of Yamaha Bikes

Augmented Reality (AR) is revolutionizing user experiences across industries, and this project, Ride Realm, integrates AR technology into the automotive sector to redefine how users interact with Yamaha bikes and scooters. The application employs cutting-edge technologies like Google ARCore and Vuforia to allow users to visualize and interact with 3D models of Yamaha vehicles in real-world environments. Key features include an AR-based catalog for product exploration, integration with the Unity engine for enhanced QR-based AR experiences, and seamless navigation to detailed product specifications via WebView. Optimized for performance with cloud rendering and designed for user-friendliness, Ride Realm bridges the gap between physical showrooms and digital platforms, enabling immersive and informative interactions with Yamaha's product range. The project underscores the transformative potential of AR in creating engaging, accessible, and innovative solutions for automotive enthusiasts. Keyword - Augmented Reality (AR), User experiences, Ride Realm, Automotive sector, Yamaha bikes and scooters, Google ARCore, Vuforia, 3D models, Real-world environments, AR-based catalog, Product exploration, Unity engine, QR-based AR experiences, WebView, Cloud rendering, User-friendliness, Physical showrooms, Digital platforms, Immersive interactions, Product range, Transformative potential, Automotive enthusiasts

Research on Lateral Stability Control of Four-Wheel Independent Drive Electric Vehicle Based on State Estimation.

This paper proposes a hierarchical framework-based solution to address the challenges of vehicle state estimation and lateral stability control in four-wheel independent drive electric vehicles. First, based on a three-degrees-of-freedom four-wheel vehicle model combined with the Magic Formula Tire model (MF-T), a hierarchical estimation method is designed. The upper layer employs the Kalman Filter (KF) and Extended Kalman Filter (EKF) to estimate the vertical load of the wheels, while the lower layer utilizes EKF in conjunction with the upper-layer results to further estimate the lateral forces, longitudinal velocity, and lateral velocity, achieving accurate vehicle state estimation. On this basis, a hierarchical lateral stability control system is developed. The upper controller determines stability requirements based on driver inputs and vehicle states, switches between handling assistance mode and stability control mode, and generates yaw moment and speed control torques transmitted to the lower controller. The lower controller optimally distributes these torques to the four wheels. Through closed-loop Double Lane Change (DLC) tests under low-, medium-, and high-road-adhesion conditions, the results demonstrate that the proposed hierarchical estimation method offers high computational efficiency and superior estimation accuracy. The hierarchical control system significantly enhances vehicle handling and stability under low and medium road adhesion conditions.

Synthesis and analysis of an integrated suspension energy harvesting system for continuous half-width speed bumps

Global warming and climate change have become an important issue that the world is focusing on due to its apparent impact on lives and economies around the world. Efforts have been put into developing more environmentally friendly vehicles ranging from hybrid to fully electrical vehicles. In addition, research efforts have been made to develop and embed energy harvesters in vehicles to reuse some of the energy usually released into the environment. This study is about the development of an energy harvesting suspension system and investigates its performance using a continuous half-width deceleration bump. A 3D vehicle model with 4 harvesters embedded in the suspension is used in the analysis. The focus is on the effect of harvesters’ parameters and speed bump’s physical characteristics on the power generation performance of the harvesters. The main objective is to maximize electricity and minimize the ride comfort efficiency index using a multi-objective optimization function in conjunction with a genetic algorithm. This study proposes an innovative self-powering method that utilizes a chassis energy harvester to convert vibrations into magnetoelectric effects. It enables autonomous green energy generation for transportation vehicles and can be applied to vehicles traversing consecutive speed bumps. In addition to effectively reducing speed, the energy harvester generates green electricity to supplement the power supply of electric vehicles.

A hybrid variable neighborhood search for a real-world petrol station replenishment problem with multi-compartment vehicles

Studies on the Petrol Station Replenishment Problem (PSRP) have many challenges because of the limitations in real-world PSRP, including diverse oil products, various vehicle models, multiple compartments in each vehicle, a finite set of vehicles, restricted tank capacities of petrol stations, and the preparation time and cost for discharge. To address those problems, this paper proposes a novel PSRP model considering the following realistic scenarios: each vehicle has multiple compartments and is allowed for multiple trips; vehicles are heterogeneous; petrol stations have hard time windows; external vehicles can be rented when internal vehicles are not enough; after arrival, each vehicle must prepare for a certain time before discharge. The objective is to minimize the overall costs including vehicles’ fixed costs, traveling costs, and preparation costs. A Hybrid Variable Neighborhood Search algorithm (HVNS) combined with a Simulated Annealing-based acceptance criterion is developed to solve the problem. Various experiments are conducted on different scale instances and a practical application of PetroChina. Experimental results show that compared with CPLEX and other heuristics, HVNS can effectively solve the problem in terms of solution quality and computing time.

Stability Study of Distributed Drive Vehicles Based on Estimation of Road Adhesion Coefficient and Multi-Parameter Control

In order to improve the driving stability of distributed-drive intelligent electric vehicles under different roadway attachment conditions, this paper proposes a multi-parameter control algorithm based on the estimation of road adhesion coefficients. First, a seven-degree-of-freedom (7-DOF) vehicle dynamics model is established and optimized with a layered control strategy. The upper-level control module calculates the desired yaw rate and sideslip angle using the two-degree-of-freedom (2-DOF) vehicle model and estimates the road adhesion coefficient by using the singular-value optimized cubature Kalman filtering (CKF) algorithm; the middle-level utilizes the second-order sliding mode controller (SOSMC) as a direct yaw moment controller in order to track the desired yaw rate and sideslip angle while also employing a joint distribution algorithm to control the torque distribution based on vehicle stability parameters, thereby enhancing system robustness; and the lower-level controller performs optimal torque allocation based on the optimal tire loading rate as the objective. A Speedgoat-CarSim hardware-in-the-loop simulation platform was established, and typical driving scenarios were simulated to assess the stability and accuracy of the proposed control algorithm. The results demonstrate that the proposed algorithm significantly enhances vehicle-handling stability across both high- and low-adhesion road conditions.

LiDAR-based Odometry Estimation Using Wheel Speed and Vehicle Model for Autonomous Buses

LiDAR-based Odometry Estimation Using Wheel Speed and Vehicle Model for Autonomous Buses

A Review of Research on the Future Development of Electric Vehicles and Traditional Fuel-powered Vehicles

As the global number of traditional fuel-powered vehicles continues to rise, the development of electric vehicles (EVs) has become increasingly significant in addressing the severe environmental pollution associated with conventional internal combustion engines. This paper presents a comparative analysis of the characteristics of electric vehicles and traditional fuel-powered vehicles, with a focus on key technological aspects such as battery replacement systems for EVs and the current advantages of fuel-powered vehicles. Furthermore, the paper examines the challenges related to the driving range of electric vehicles, as well as the environmental impact of fuel-based transportation. Drawing on these analyses, market equilibrium strategies are proposed for the future development of both electric and fuel vehicle models. Considering the respective advantages and limitationssuch as the battery life constraints of EVs and the environmental pollution from fuel vehiclesthis paper outlines a roadmap for the coexistence and progression of both technologies. The findings suggest that while fuel-powered vehicles may continue to play a role, electric vehicles are poised to dominate the civilian automotive market in the future.

Study on the Dynamic Response of the Carbody–Anti-Bending Bars System

Ride comfort is an important requirement that passenger rail vehicles must meet. Carbody–anti-bending system is a relatively new passive method to enhance the ride comfort in passenger rail vehicles with long and light carbody. The resonance frequency of the first bending mode (FBM) of such vehicle is within the most sensitive frequency range that affects ride comfort. Anti-bending bars consist of two bars that are mounted under the longitudinal beams of the carbody chassis using vertical supports. When the carbody bends, the anti-bending bars develop moments in the neutral axis of the carbody opposing the bending of the carbody. In this way, the carbody structure becomes stiffer and the resonance frequency of the FBM can be increased beyond the upper limit of the discomfort range of frequency, improving the ride comfort. The theoretical principle of this method has been demonstrated employing a passenger rail vehicle model that includes the carbody as a free–free Euler–Bernoulli beam and the anti-bending bars as longitudinal springs jointed to the vertical supports. Also, the method feasibility has been verified in the past using an experimental scale demonstrator system. In this paper, a new model of the carbody–anti-bending bar system is proposed by including three-directional elastic elements (vertical and longitudinal direction and rotation in the vertical–longitudinal plane) to model the fastening of the anti-bending bars to the supports and the vertical motion of the anti-bending bars modelled as free–free Euler–Bernoulli beams connected to the elastic elements of the fastening. In the longitudinal direction, the anti-bending bars work as springs connected to the longitudinal elastic elements of the fastening. The modal analysis method is applied to point out the basic properties of the frequency response functions (FRFs) of the carbody–anti-bending bars system, considering the bounce and FBMs of both the carbody and the anti-bending bars. A parametric study of the FRF of the carbody shows that the vertical stiffness of the fastening should be sufficiently high enough to eliminate the influence of the modes of the anti-bending bars upon the carbody response and to reduce the anti-bending bars vibration in the frequency range of interest. Longitudinal stiffness of the elastic elements of the fastening is critical to increase the bending resonance frequency of the carbody out of the sensitive range. Longer anti-bending bars can improve the capability of the anti-bending bars to increase the bending resonance without the risk of interference effects caused by the bounce and bending modes of the anti-bending bars.