Search Results

Abstract Taking the semi-active suspension system as the research object, the forward model and inverse model of a continuous damping control (CDC) damper are established based on the characteristic test of the CDC damper. A multi-mode semi-active suspension controller is designed to meet the diverse requirements of vehicle performance under different road conditions. The controller parameters of each mode are determined using a genetic algorithm. In order to achieve automatic switching of the controller modes under different road conditions, a method is proposed to identify the road roughness based on the sprung mass acceleration. The average of the ratio between the squared sprung mass acceleration and the vehicle speed within a specific time window is taken as the identification indicator for road roughness.

Abstract In order to efficiently predict and investigate a vehicle’s vertical dynamics, it is necessary to consider the suspension component properties holistically. Although the effects of suspension stiffness and damping characteristics on vertical dynamics are widely understood, the impact of suspension friction in various driving scenarios has rarely been studied in both simulation and road tests for several decades. The present study addresses this issue by performing driving tests using a special device that allows a modification of the shock absorber or damper friction, and thus the suspension friction to be modified independently of other suspension parameters. Initially, its correct functioning is verified on a shock absorber test rig. A calibration and application routine is established in order to assign definite additional friction forces at high reproducibility levels.

Abstract This article introduces a methodology for conducting comparative evaluations of vibration-induced discomfort. The aim is to outline a procedure specifically focused on assessing and comparing the discomfort caused by vibrations. The article emphasizes the metrics that can effectively quantify vibration-induced discomfort and provides insights on utilizing available information to facilitate the assessment of differences observed during the comparisons. The study also addresses the selection of appropriate target scenarios and test environments within the context of the comparative evaluation procedure. A practical case study is presented, highlighting the comparison of wheel corner concepts in the development of new vehicle architectures. Currently, the evaluation criteria and difference thresholds available allow for comparative evaluations within a limited range of vehicle vibration characteristics.

Abstract Due to the presence of uncertain disturbances in the actual steering system, disturbances in the system may affect the handling stability of the vehicle. Therefore, this article proposes an integrated steering system control strategy with stronger anti-disturbance performance. When disturbances exist in the system, the proposed control strategy effectively reduces the attitude changes during the vehicle steering process. In the upper-level control strategy, a variable transmission ratio curve is designed to coordinate the high-speed handling stability and low-speed steering sensitivity of the vehicle. On this basis, a sideslip angle observer is proposed based on the extended state observation theory, which does not depend on an accurate system model, thus determining the intervention timing of the active front wheel steering system. In the lower-level control strategy, DR-PI/DR-PID controllers are designed for the integrated steering system.

Abstract A time domain analysis method of ride comfort and energy dissipation characteristics is proposed for automotive vibration proportional–integral–derivative (PID) control. A two-degrees-of-freedom single wheel model for automotive vibration control is established, and the conventional vibration response variables for ride comfort evaluation and the energy consumption vibration response variables for energy dissipation characteristics evaluation are determined, and the Routh stability criterion method was introduced to assess the impact of PID control on vehicle stability. The PID control parameters are tuned using the differential evolution algorithm, and to improve the algorithm’s adaptive ability, an adaptive operator is introduced, so that the mutation factor of differential evolution algorithm can change with the number of iterations.

Abstract An automatic collision avoidance control method integrating optimal four-wheel steering (4WS) and direct yaw-moment control (DYC) for autonomous vehicles on curved road is proposed in this study. Optimal four-wheel steering is used to track a predetermined trajectory, and DYC is adopted for vehicle stability. Two single lane change collision avoidance scenarios, i.e., a stationary obstacle in front and a moving obstacle at a lower speed in the same lane, are constructed to verify the proposed control method. The main contributions of this article include (1) a quintic polynomial lane change trajectory for collision avoidance on curved road is proposed and (2) four different kinds of control method for autonomous collision avoidance, namely 2WS, 2WS+DYC, 4WS, and 4WS+DYC, are compared. In the design of DYC controller, two different feedback control methods are adopted for comparison, i.e., sideslip angle feedback and yaw rate feedback.

Abstract In order to enhance the path-tracking accuracy and adaptability of the electric flatbed vehicle (EFV) in the soft-target vehicle test system, an improved controller is designed based on the linear quadratic regulator (LQR) algorithm. First, the LQR feedback controller is designed based on the EFV dynamics tracking error model, and the genetic algorithm is utilized to obtain the optimal weight coefficient matrix for different speeds. Second, a weight coefficient matrix compensation strategy is proposed to address the changes in the relationship between the vehicle–road position and attitude caused by external disturbances and the state of EFV. An offline parameter table is created to reduce the computational load on the microcontroller of EFV and enhance real-time path-tracking performance. Furthermore, an adaptive preview time control strategy is added to reduce the overshooting caused by control delay. This strategy is based on road curvature and traveling speed.

Abstract Accurate estimation of traction force is essential for the development of advanced control systems, particularly in the domain of autonomous driving. This study presents an innovative approach to enhance the estimation of tire–road interaction forces under combined slip conditions, employing a combination of empirical models and neural networks. Initially, the well-known Pacejka formula, or magic formula, was adopted to estimate tire–road interaction forces under pure longitudinal slip conditions. However, it was observed that this formula yielded unsatisfactory results under non-pure slip conditions, such as during curves. To address this challenge, a neural network architecture was developed to predict the estimation error associated with the Pacejka formula. Two distinct neural networks were developed. The first neural network employed, as inputs, both longitudinal slip ratios of the driving wheels and the slip angles of the driving wheels.

Abstract Vehicle-to-infrastructure (V2I) connectivity technology presents the opportunity for vehicles to perform autonomous longitudinal control to navigate safely and efficiently through sequences of V2I-enabled intersections, known as connected corridors. Existing research has proposed several control systems to navigate these corridors while minimizing energy consumption and travel time. This article analyzes and compares the simulated performance of three different autonomous navigation systems in connected corridors: a V2I-informed constant acceleration kinematic controller (V2I-K), a V2I-informed model predictive controller (V2I-MPC), and a V2I-informed reinforcement learning (V2I-RL) agent. A rules-based controller that does not use V2I information is implemented to simulate a human driver and is used as a baseline. The performance metrics analyzed are net energy consumption, travel time, and root-mean-square (RMS) acceleration.

Abstract The European Union’s pro-ecological policy imposes a requirement to use biofuel additives in diesel fuel which is supposed to support the sustainable development of transport and limit its negative impact on the natural environment. The study presents an analysis of the exhaust gas components and the amount of solid particles carried out for internal combustion engines fueled with mixtures of diesel fuel and fatty acid methyl esters. Additionally, the computer software of the tested power units was modified by changing the amount of fuel to be supplied and the air intake. The goal of the tests was to find out how the fuel mixture and reprogramming of the computer control systems would impact the emission of exhaust gas components. Based on the tests, it was found that an additive of fatty acid methyl esters to diesel does have an influence on the tested unit parameters.

Abstract Adaptive neural networks (ANNs) have become famous for modeling and controlling dynamic systems. However, because of their failure to precisely reflect the intricate dynamics of the system, these have limited use in practical applications and perform poorly during training and testing. This research explores novel approaches to this issue, including modifying the simple neuron unit and developing a generalized neuron (GN). The revised version of the neuron unit helps to develop the system controller, which is responsible for providing the desired control signal based on the inputs received from the dynamic responses of the vehicle suspension system. The controller is then tested and evaluated based on the performance of the magnetorheological (MR) damper for the main suspension system.

Abstract For spacecraft with high power consumption, it is reasonable to build the thermal control system based on a two-phase mechanically pumped loop. The heat-controlled accumulator is a key element of the two-phase mechanically pumped loop, which allows for the control of pressure in the loop and maintains the required level of coolant boiling temperature or cavitation margin at the pump inlet. There can be two critical modes of loop operation where the ability to control pressure will be lost. The first critical mode occurs when the accumulator fills with liquid at high heat loads. The second critical mode occurs when the accumulator is at low heat loads and partial loss of coolant, for example, due to the leak caused by micrometeorite breakdown. Both modes are caused by insufficient accumulator volume or working fluid charge.

Abstract Letter from the Special Issue Editors

Abstract In the last decades we have witnessed an increasing number of military operations in urban environments. Complex urban operations require high standards of training, equipment, and personnel. Emergency forces on the ground will need specialized vehicles to support them in all parts and levels of this extremely demanding environment including the subterranean and interior of infrastructure. The development of vehicles for this environment has lagged but offers a high payoff. This article describes the method for developing a concept for an urban operations vehicle by characterization of the urban environment, deduction of key issues, evaluation of related prototyping, science fiction story-typing of the requirements for such a vehicle, and comparison with field-proven and scalable solutions. Embedding these thoughts into a comprehensive research and development program provides lines of development, setting the stage for further research.

Abstract A precise knowledge of the road profile ahead of the vehicle is required to successfully engage a proactive suspension control system. If this profile information is generated by preceding vehicles and stored on a server, the challenge that arises is to accurately determine one’s own position on the server profile. This article presents a localization method based on a particle filter that uses the profile observed by the vehicle to generate an estimated longitudinal position relative to the reference profile on the server. We tested the proposed algorithm on a quarter vehicle test rig using real sensor data and different road profiles originating from various types of roads. In these tests, a mean absolute position error of around 1 cm could be achieved. In addition, the algorithm proved to be robust against local disturbances, added noise, and inaccurate vehicle speed measurements.

Abstract The electrically interconnected suspension (EIS) is a novel suspension system that has gained attention due to its potential to improve vehicle vibration control. This article provides a comprehensive review of EIS and related technologies. It starts with an overview of the research on hydraulic interconnected suspension (HIS) and its limitations. Then, it discusses the development of the electromagnetic suspension (EMS) and its advantages in adjusting mechanical characteristics. The article focuses on the electrical network and decoupling control characteristics of EIS, demonstrating the principle of synchronous decoupling control of multiple vibration modes. A comparison of the structure and control characteristics of EIS and HIS highlights the advantages of EIS in vehicle vibration control.

Abstract The tailpipe zero-emission legislation has pushed the automotive industry toward more electrification. Regenerative braking is the capability of electric machines to provide brake torque. So far, the regenerative braking feature is primarily considered due to its effect on energy efficiency. However, using individual e-machines for each wheel makes it possible to apply the antilock braking function due to the fast torque-tracking characteristics of permanent magnet synchronous motors (PMSM). Due to its considerable cost reduction, in this article, a feasibility study is carried out to investigate if the ABS function can be done purely through regenerative braking using a mid-fidelity model-based approach. An uni-tire model of the vehicle with a surface-mount PMSM (SPMSM) model is used to verify the idea. The proposed ABS control system has a hierarchical structure containing a high-level longitudinal slip controller and a low-level SPMSM torque controller.

Abstract Three suspension structures including the parallel vertical suspension (PVS), the horizontal parallel suspension (HPS), and the negative stiffness element added into suspension (NSES) of the driver’s seat are proposed to improve the driver’s ride comfort of off-road vehicles. Based on the dynamic models of the PVS, HPS, and NSES established and simulated under the same random excitations of the cab floor, the effect of the design parameters of the proposed models is analyzed, and the design parameters are then optimized to evaluate their isolation performance. The indexes of the root-mean-square (r.m.s) accelerations of the vertical seat direction, pitching seat angle, and rolling seat angle are used as the objective functions. The study results indicate that the dynamic parameters of the PVS, HPS, and NSES greatly affect the driver’s ride comfort while their geometrical dimensions insignificantly affect the driver’s ride comfort.

Abstract Brushless direct current (BLDC) motor aims to obtain high efficiency when compared to conventional DC motors due to several reasons. But when it comes to the control then its control is much more complicated due to the requirement of a phase supply switching circuit. Usually, the conventional and classical proportional integral derivative (PID) controller is used but it is quite cumbersome to tune its fixed gains. APID controller is used where PID fails to fulfill the objectives in varying situations. So, the adaptive proportional integral derivative (APID) controller is utilized to enhance the results. An artificial neural network (ANN) controller is one of the recent control methods, which gives accurate and precise results and utilizes ANN to give more accurate results. But it lacks fuzzy logic, that is, human tendency, and finally, the artificial neuro-fuzzy inference system (ANFIS) controller is concluded as the best controller to limit the speed of the BLDC motor.

Abstract Advances made in advanced driver assistance systems such as antilock braking systems (ABS) have significantly improved the safety of road vehicles. ABS enhances the braking and steerability of a vehicle under severe braking conditions. However, ABS performance degrades on rough roads. This is largely due to noisy measurements, the type of ABS control algorithm used, and the excitation of complex dynamics such as higher-order tire mode shapes that are neglected in the control strategy. This study proposes a model-free intelligent control technique with no modelling constraints that can overcome these unmodelled dynamics and parametric uncertainties. The double deep Q-learning network (DDQN) algorithm with the temporal convolutional network is presented as the intelligent control algorithm. The model is initially trained with a simplified single-wheel model.