Search Results (14)

Effective upper-extremity rehabilitation requires intensive and precise movement training, yet conventional therapies lack accurate motion tracking. Robotic exoskeletons address this limitation but are often hindered by ergonomic misalignment and limited adaptability. The AssistOn-Arm, a novel self-aligning exoskeleton, integrates ergonomic design and back-drivable actuation to enhance comfort and facilitate natural user interaction. This study aimed to assess the usability and ergonomics of the device in healthy participants and to conduct a pilot clinical evaluation in individuals with upper-extremity impairments. Thirty healthy participants and twelve patients with shoulder impairments performed predefined tasks under participant-active and device-active conditions. Kinematic data captured concurrently with AssistOn-Arm and Xsens MVN demonstrated strong agreement between conditions. Quantitative analysis revealed no significant differences (p > 0.05) in flexion, elevation, abduction–adduction, and external rotation, indicating reliable alignment with natural joint axes. Significant differences (p < 0.05) were observed only in sagittal hyperextension and internal rotation, reflecting device mechanical constraints. The study confirms the clinical feasibility of AssistOn-Arm as a sensor-driven, self-aligning exoskeleton that bridges engineering innovation and precision rehabilitation, paving the way for its integration into clinical practice. Full article

Trajectory planning for autonomous vehicles is essential for ensuring driving safety, passenger comfort, and operational efficiency. Collision avoidance constraints introduce significant computational complexity due to their inherent non-convex and nonlinear characteristics. Previous research has proposed the drivable corridor (DC) method, which transforms complex collision avoidance constraints into linear inequalities by constructing time-varying rectangular corridors within the spatiotemporal domains, thereby enhancing optimization efficiency. However, the DC construction process involves repetitive collision detection, leading to an increased computational burden. To address this limitation, this study proposes a novel approach that integrates grid-based obstacle representation with dynamic grid merging to accelerate collision detection and dynamically constructs the DC by adaptively adjusting the expansion strategies according to available spatial dimensions. The feasibility and effectiveness of the proposed method are validated through simulation-based evaluations conducted over 100 representative scenarios characterized by diverse and unstructured environmental configurations. The simulation results indicate that, with appropriately selected grid resolutions, the proposed approach achieves up to a 60% reduction in trajectory planning time compared to conventional DC-based planners while maintaining robust performance in complex environments. Full article

The time that a vehicle merges in a lane reduction can significantly affect passengers’ safety, comfort, and energy consumption, which can, in turn, affect the global adoption of autonomous electric vehicles. In this regard, this paper analyzes how connected and automated vehicles should cooperatively drive to reduce energy consumption and improve traffic flow. Specifically, a model-free deep reinforcement learning approach is used to find the optimal driving behavior in the scenario in which two platoons are merging into one. Several metrics are analyzed, including the time of the merge, energy consumption, and jerk, etc. Numerical simulation results show that the proposed framework can reduce the energy consumed by up to 76.7%, and the average jerk can be decreased by up to 50%, all by only changing the cooperative merge behavior. The present findings are essential since reducing the jerk can decrease the longitudinal acceleration oscillations, enhance comfort and drivability, and improve the general acceptance of autonomous vehicle platooning as a new technology. Full article

The powertrain of a car plays a major role in establishing the vehicle’s offered comfort due to vibrations because it is the heaviest single component installed on the chassis; therefore, when oscillating, it transmits considerable forces to the chassis, inducing unwanted vibrations. For this reason, it is important to identify some associated properties with the powertrain suspension system that describe the performance of its rigid body dynamics. In this way, we could place constraints and requirements on these quantities in order to exclude all the configurations that cause intolerable levels of vibrations, and include all the others in the analysis for further evaluation. The definition of these requirements is critical: a poor setting of requirements excludes potentially good powertrain suspension setups and includes those ones with a drivability index that is too poor. In this paper, we identify a set of quantities that show correlation with the vibration performance of the powertrain setup. A method for testing the specificity of the requirements is also shown in order to evaluate which requirements perform best when it comes to filtering engine suspension setups that provide an acceptable level of vibrations. Full article

Electric vehicle drivetrains are considered a way to reduce greenhouse gas emissions from road traffic. The use of electric drives in automotive vehicles offers advantages, such as the potential to recover energy during braking (regenerative braking). The limitation of the maximum air gap torque of the vehicle drive machine by several factors requires a temporary standalone or simultaneous use of the conventional vehicle wheel brake. In several studies, it is shown that during braking operations, the drive machine and the vehicle wheel brake can induce torsional oscillations in the drivetrain, which have a negative influence on the driving comfort and lead to a high mechanical load. To reduce these oscillations, the simultaneous use of an active anti-jerk control is necessary. Due to the problem of oscillation excitations caused by a brake intervention, the used drivability function (integrated prefilter, anti-jerk control) is investigated and optimized with regard to brake blending maneuvers and the effectiveness for damping torsional oscillations. Therefore, the dynamics of the drivetrain are adapted to the dynamics of the braking system using the prefilter, which leads to precise fulfilment of the driver’s braking desire, even during dynamic brake blending maneuvers. All investigations are carried out with a hardware-in-the-loop test bench to create reproducible results. Full article

Vehicles with dual clutch transmissions (DCT) are well known for their comfortable drivability since gear shifts can be performed jerklessly. The ability of blending the torque during gear shifts from one clutch to the other, making the type of automated transmission a perfect alternative to torque converters, which also comes with a higher efficiency. Nevertheless, DCT also have some drawbacks. The actuation of two clutches requires an immense control effort, which is handled in the implementation of a wide range of software functions on the transmission control unit (TCU). These usually contain control parameters, which makes the behavior adaptable to different vehicle and engine platforms. The adaption of these parameters is called calibration, which is usually an iterative time-consuming process. The calibration of the embedded software solutions in control units is a widely known problem in the automotive industry. The calibration of any vehicle subsystem (e.g., engine, transmission, suspension, driver assistance systems for autonomous driving, etc.) requires costly test trips in different ambient conditions. To reduce the calibration effort and the accompanying use of professionals, several approaches to automize the calibration process are proposed. Due to the fact that a solution is desired which can optimize different calibration problems, a generic metaheuristic approach is aimed. Regardless, the scope of the current research is the optimization of the launch behavior for vehicles equipped with DCT since, particularly at low speeds, the transmission behavior must meet the intention of the driver (drivers tend to be more perceptive at low speeds). To clarify the characteristics of the launch, several test subject studies are performed. The influence factors, such as engine sound, maximal acceleration, acceleration build-up (mean jerk), and the reaction time, are taken into account. Their influence on the evaluation of launch with relation to the criteria of sportiness, comfort, and jerkiness, are examined based on the evaluation of the test subject studies. According to the results of the study, reference values for the optimization of the launch behavior are derived. The research contains a study of existing approaches for optimizing driving behavior with metaheuristics (e.g., genetic algorithms, reinforcement learning, etc.). Since the existing approaches have different drawbacks (in scope of the optimization problem) a new approach is proposed, which outperforms existing ones. The approach itself is a hybrid solution of reinforcement learning (RL) and supervised learning (SL) and is applied in a software in the loop environment, and in a test vehicle. Full article

Since the mid-2000 s, longitudinal tinning has been applied to concrete pavements of expressways to improve the comfort and safety of road vehicle users. However, in certain longitudinal tinning sections, lateral vibrations occur during driving, which poses a safety hazard. This paper analyzes the cause behind this problem and proposes a longitudinal texturing specification that can minimize lateral vibration. To perform this analysis, the accelerations of driving vehicles and the degree of lateral vibration transmitted to panels in the vehicles were evaluated for each longitudinal texture applied in South Korea. Structural analysis was also conducted for the lateral force and moment according to the tire tread profile and longitudinal texturing specifications using the finite element method. In addition, field investigation, indoor drivability evaluation, and field application were performed to evaluate the optimal longitudinal texturing specification. The results indicate that the texture designated as 2 × 3 × 19 mm is the optimal longitudinal texture on hardened concrete pavement. However, because this specification is difficult to apply to fresh concrete, 3 × 3 × 16 mm is proposed as the optimal longitudinal texture owing to its excellent performance in the panel survey and in structural analysis. Full article

For parallel hybrid electric vehicles (HEVs), the clutch serves as a vital enabling actuator element during mode transitions. The expected drivability and smoothness of parallel HEVs are difficult to be achieve owing to the neglect of clutch-torque-induced disturbance and different response characteristics of power sources during clutch slipping. To address this issue, this paper proposes a novel control strategy to coordinate the engine and motor during the clutch slipping process. A sliding mode control strategy based on a group-preserving scheme was applied to control the motor. The vehicle dynamic equation was constructed by the sliding surface with the Lagrange function. The equation solutions obtained by introducing the Runge–Kutta method were used as motor control inputs. Meanwhile, an adaptive PI controller was designed to regulate engine speed for the reduction in the speed difference of the clutch. The hardware-in-the-loop simulations were conducted to validate the outstanding performance of the proposal strategy. The verification results indicate that the proposed strategy not only reduces the vehicle jerk and frictional losses effectively, but also improves vehicle driving comfort and reliability. Full article

This paper presents the modelling and calculations for a hybrid electric vehicle (HEV) in parallel configuration, including a main electrical driving motor (EM), an internal combustion engine (ICE), and a starter/generator motor. The modelling equations of the HEV include vehicle acceleration and jerk, so that simulations can investigate the vehicle drivability and comfortability with different control parameters. A model predictive control (MPC) scheme with softened constraints for this HEV is developed. The new MPC with softened constraints shows its superiority over the MPC with hard constraints as it provides a faster setpoint tracking and smoother clutch engagement. The conversion of some hard constraints into softened constraints can improve the MPC stability and robustness. The MPC with softened constraints can maintain the system stability, while the MPC with hard constraints becomes unstable if some input constraints lead to the violation of output constraints. Full article

This work is part of the project called “Gölem project”, started in 2017, about special devices developed to enable the so-called Accessible Tourism. This project aims to design and develop a trekking wheelchair for people with impaired mobility. After an initial phase of design and prototyping, the testing phase has now begun. The objective is to validate several aspects of the design, concerning basic kinematics and dynamics, passenger comfort and physical effort of the carriers. This paper describes the development of qualitative tests for drivability and balance validation of this first prototype. At this stage, a list of features to be investigated was made, suitable trekking paths were chosen, and qualitative experimental field tests were performed. Then, the design of the prototype was modified according to these first experimental results, to improve the wheelchair characteristics. The prototype is now undergoing the modification phase, then further testing will be performed with the use of specific instrumental devices to evaluate the wheelchair itself and to perform the kinematic, dynamical, and comfort characterization. Full article

This paper presents an innovative electronically controlled suspension system installed on a real car and used as a test bench. The proposed setup relies on a sensor network that acquires a large real-time dataset collecting the car vibrations and the car trim and, through a new controller based on a recently proposed theory developed by the authors, makes use of adjustable semi-active magneto-rheological dampers. A BMW series 1 is equipped with such an integrated sensors-controller-actuators device and an extensive test campaign, in real driving conditions, is carried out to evaluate its performance. Thanks to its strategy, the new plant enhances, at once, both comfort and drivability of the car, as field experiments show. A benchmark analysis is performed, comparing the performance of the new control system with the ones of traditional semi-active suspensions, such as skyhook devices: the comparison shows very good results for the proposed solution. Full article

The energy management strategy has a great influence on the fuel economy of hybrid electric vehicles, and the equivalent consumption minimization strategy (ECMS) has proved to be a useful tool for the real-time optimal control of Hybrid Electric Vehicles (HEVs). However, the adaptation of the equivalent factor poses a major challenge in order to obtain optimal fuel consumption as well as robustness to varying driving cycles. In this paper, an adaptive-ECMS based on driving pattern recognition (DPR) is established for hybrid electric vehicles with continuously variable transmission. The learning vector quantization (LVQ) neural network model was adopted for the on-line DPR algorithm. The influence of the battery state of charge (SOC) on the optimal equivalent factor was studied under different driving patterns. On this basis, a method of adaptation of the equivalent factor was proposed by considering the type of driving pattern and the battery SOC. Besides that, in order to enhance drivability, penalty terms were introduced to constrain frequent engine on/off events and large variations of the continuously variable transmission (CVT) speed ratio. Simulation results showed that the proposed method efficiently improved the equivalent fuel consumption with charge-sustaining operations and also took into account driving comfort. Full article

Plug-in hybrid electric buses (PHEBs) is some of the most promising products to address air pollution and the energy crisis. Considering the switching between different working modes often bring about sudden changes of the torque and the speed of different power sources, which may lead to the instability of the power output and affect the driving performance and ride comfort, it is of great significance to develop a real-time optimal energy management strategy for PHEBs to achieve the optimization of fuel economy and drivability. In this study, the proposed strategy includes an offline part and an online part. In the offline part, firstly, the energy conversion coefficient s(t) is optimized by linear weight particle swarm optimization algorithm (LinWPSO), then, the optimization results of s(t) are converted into a 2-dimensional look-up table. Secondly, combined with three typical driving cycle conditions, the gear-shifting correction and mode switching boundary parameters that affect the drivability of the vehicle are extracted by dynamic programming (DP) algorithm. In the online part, combined with the s(t), the gear-shifting correction and mode switching boundary parameters which are obtained through offline optimization, the real-time energy management strategy is proposed to solve the trade-off problem between minimizing the fuel consumption and improving the drivability and riding comfort. Finally, the proposed strategy is verified with simulation, the results show that the proposed strategy can guarantee the engine and the electric motor (EM) work in the high-efficiency area with optimal energy distribution while keeping drivability in the variation of driving circle. The overall performance is improved by 18.54% compared with the rule-based control strategy. The proposed strategy may provide theoretical support for the optimal control of PHEB. Full article

Drivetrain hybridization implies adding a secondary power source (electric machine/battery) to a primary power source (engine/filled fuel tank) in order to improve: fuel economy, emissions, drivability (performance), comfort and safety. Designing a hybrid vehicle drivetrain fulfilling the required vehicle driving functions is therefore a complex task. Many researchers have put effort formulating and developing overall hybrid drivetrain analysis, design and optimization models including top-level vehicle control strategy for optimal fuel economy. This paper seeks to investigate the possibility of overall model simplification for the hybrid drivetrain system including the control strategy. This is performed by describing the component efficiencies and control rules with only a few characteristic parameters that capture the total systems fuel efficiency with sufficient accuracy (~1%). Using these parameters the modeling and simulation process can be done very quickly. The method has been demonstrated on a series -, a parallel - and a series-parallel hybrid drivetrain with specified component technologies, vehicle parameters and drive cycle. The fuel economy and control strategy results are compared with Simulink/Advisor and Dynamic Programming. Full article