Search Results (70)

This study aims to develop and evaluate a cycling-specific marker protocol that minimises the number of markers while accounting for the unique biomechanics of cycling. Although movements in the frontal and transverse planes during cycling are limited, they are clinically relevant due to their association with overuse injuries. Existing gait-based marker protocols often fail to consider cycling-specific factors such as posture, range of motion, marker occlusion, and muscle-induced artifacts. The proposed protocol (PP) uses 15 physical and 8 virtual markers. In the absence of a gold standard for 3D pedalling kinematics, the PP was evaluated by comparing it with established gait analysis protocols. The protocol demonstrated high correlation in gait (CCC > 0.98 for hip and knee in the sagittal plane), low intra-subject variability (CV < 15% for hip, knee, and ankle), and high repeatability. During pedalling, position, velocity, and acceleration were measured in all three spatial directions. Notably, angular velocity and linear acceleration showed significant components outside the sagittal plane, particularly for angular velocity. These findings highlight the importance of considering 3D motion when estimating forces, joint moments, and joint-specific powers in cycling biomechanics. Full article

Background: The aging population in Japan has led to an increase in traffic accidents involving elderly drivers, highlighting the need for measures to enhance driving safety. Post-total knee arthroplasty (TKA) patients must regain their driving ability to maintain independence; however, clear guidelines for driving resumption are lacking. This study assessed the movement time (MT) and brake pedal force (BPF) using a driving simulator and investigated their associations with lower limb motor function. Methods: This single-center prospective cohort study included 21 patients (mean age: 66.7 ± 7.4 years) who underwent right TKA and intended to resume driving. Driving ability was assessed on postoperative day 13 using a driving simulator to measure MT and BPF. Physical function was evaluated using the following parameters: range of motion (ROM), muscle strength, gait parameters, and pain assessment. Pearson’s correlation and multiple regression analyses were performed to identify significant associations. Results: MT was significantly correlated with knee extension strength (r = −0.56, p = 0.02) and walking ratio (r = 0.55, p = 0.03). BPF was significantly correlated with walking ratio (r = 0.52, p = 0.04) and pain levels VAS (r = −0.54, p = 0.02). Multiple regression analysis identified walking ratio (β = 0.54, p = 0.02) as a significant predictor of MT. For BPF, significant predictors included walking ratio (β = 0.49, p = 0.03) and VAS (β = −0.54, p = 0.02). Discussion: The findings of this study suggest that MT is associated with walking ratio, while BPF is significantly associated with both walking ratio and VAS scores. In particular, walking ratio was found to have a significant impact on both MT and BPF, indicating that it may be an important factor influencing postoperative driving performance. Conclusion: Improvement in the walking ratio and pain management affect accelerator and brake operation during driving after TKA. Full article

The equivalent factor (EF) of adaptive equivalent consumption minimization strategy (A-ECMS) has a direct impact on the performance of hybrid electric trucks (HETs). Although EF on the state of charge (SoC) can effectively achieve fuel economy and SoC maintenance, battery life issues still need to be considered. Battery replacement costs are extremely high, directly affecting the operational costs of HETs. Thus, A-ECMS with enhanced battery life (A-ECMS-EBL) is proposed. Firstly, the near-optimal boundary of EF is determined to ensure the fuel economy of A-ECMS-EBL by analyzing the working mechanism of the HET powertrain. Secondly, a new EF calculation method is developed to enhance battery life. This method utilizes accelerator pedal opening (APO) feedback to optimize the power distribution between the engine and battery under high load conditions, thereby reducing the ratio of battery output power and number of battery cycle (NBC). Finally, the simulation results show that under typical cycle conditions, the equivalent fuel consumption (EFC) of A-ECMS-EBL increased by only 2.3% compared to the dynamic programming (DP), decreased by 1.1% compared to the A-ECMS, and the NBC significantly decreased by 6.12%. The results indicate that A-ECMS-EBL has significant advantages in improving fuel economy and enhancing battery life. Full article

With the increasing emphasis on environmental sustainability, the electrification of urban public bus fleets has gained significant attention. Understanding the factors influencing the energy consumption of battery-electric buses (BEBs) is crucial for enhancing their energy efficiency. Therefore, it is crucial to identify the subsystems that contribute most to energy consumption and understand how operational factors influence them. This paper presents a comprehensive analysis of BEB energy consumption based on experimental measurements performed with a 12 m fully electric battery bus. The main limitations of this study stem from the use of a single vehicle over a total period of 18 days, during which 187 routes were completed. Additionally, sandbags were used as ballast in place of actual passengers. Various parameters, including the number of passengers, drivers, route characteristics, environmental conditions, and traffic, were analyzed to assess their impact on BEB energy consumption. Data related to the energy consumed by various bus utilities were collected through the vehicle’s CAN network, with a sampling rate of 1 measurement per second. These data were analyzed both daily and per route, revealing the breakdown of energy consumption among different utilities and highlighting those responsible for the highest energy use. The results correlate the total distance traveled, service duration, average speed, driver’s driving style, route characteristics, internal and external temperatures, and air-conditioning system’s reference temperature with the energy consumption of the traction motors and climate control system. In addition, the correlation between the driver, vehicle acceleration, and throttle pedal use, and the energy consumed by the electric traction motor is presented. Full article

Background/Objectives: The purpose of this research was to create a peak detection algorithm and machine learning model for use in triathlon. The algorithm and model aimed to automatically measure movement cadence in all three disciplines of a triathlon using data from a single inertial measurement unit and to recognise the occurrence and duration of cycling task changes. Methods: Six triathletes were recruited to participate in a triathlon while wearing a single trunk-mounted measurement unit and were filmed throughout. Following an initial analysis, a further six triathletes were recruited to collect additional cycling data to train the machine learning model to more effectively recognise cycling task changes. Results: The peak-counting algorithm successfully detected 98.7% of swimming strokes, with a root mean square error of 2.7 swimming strokes. It detected 97.8% of cycling pedal strokes with a root mean square error of 9.1 pedal strokes, and 99.4% of running strides with a root mean square error of 1.2 running strides. Additionally, the machine learning model was 94% (±5%) accurate at distinguishing between ‘in-saddle’ and ‘out-of-saddle’ riding, but it was unable to distinguish between ‘in-saddle’ riding and ‘coasting’ based on tri-axial acceleration and angular velocity. However, it displayed poor sensitivity to detect ‘out-of-saddle’ efforts in uncontrolled conditions which improved when conditions were further controlled. Conclusions: A custom peak detection algorithm and machine learning model are effective tools to automatically analyse triathlon performance. Full article

The current trend in pure electric heavy-duty commercial vehicles (PEHCVs) is the increasing utilization of automated manual transmission (AMT) to optimize driveline efficiency. However, the existing gear-shift schedule of AMT fails to account for crucial factors such as vehicle load and slope gradient, leading to frequent gear position changes during uphill driving, compromising driving comfort. This study proposes a novel approach incorporating the vehicle’s load and slope gradient to develop an enhanced gear-shift strategy based on fuzzy logic control to address this issue more effectively. Initially, a dynamic gear-shift schedule was formulated for a 6-speed AMT-equipped PEHCV, followed by an analysis of the impact of vehicle load and slope gradient on the gear-shift schedule. Subsequently, an adaptive gear-shift design framework was developed using fuzzy logic control, considering inputs such as acceleration pedal opening, vehicle load, and slope gradient. Simultaneously, the velocity correction factor was designed as an output to adjust the velocity of gear-shift points based on the dynamic gear-shift schedule. Finally, simulations were conducted under various operating scenarios, including different slope gradients, varying vehicle loads, changing pedal openings, and random scenarios to compare and validate the proposed gear-shift schedule against its predecessor—the previous dynamic gear-shift schedule. The results demonstrate that the proposed gear-shift schedule exhibits exceptional adaptability to various driving scenarios. The average acceleration time can be reduced by over 20%, while the gear-shift frequency within 200 s can be decreased by more than 30 times. Full article

Urban transportation systems, particularly underground interchanges, present significant challenges for sustainable and resilient urban design due to their complex road geometries and dense traffic signage. These challenges are further compounded by the interaction of diverse road users, which heightens the risk of accidents. To enhance both safety and sustainability, this study integrates advanced driving simulation techniques with machine learning models to improve driving safety and comfort in underground interchanges. By utilizing a driving simulator and 3D modeling, real-world conditions were replicated to design key traffic safety features with an emphasis on sustainability and driver well-being. Critical safety parameters, including speed, acceleration, and pedal use, were analyzed alongside comfort metrics such as lateral acceleration and steering torque. The LightGBM machine learning model was used to classify safety and comfort grades with an accuracy of 97.06%. An important ranking identified entrance signage and deceleration zones as having the greatest impact on safety and comfort, while basic road sections were less influential. These findings underscore the importance of considering visual cues, such as markings and wall color, in creating safer and more comfortable underground road systems. This study’s methodology and results offer valuable insights for urban planners and engineers aiming to design transportation systems that are both safe and aligned with sustainable urban mobility objectives. Full article

Accurate longitudinal control is crucial in autonomous driving, but inherent delays and lag in electric vehicle powertrains hinder precise control. This paper presents a two-stage design for a longitudinal speed controller to enhance speed tracking performance in autonomous electric vehicles. The first stage involves designing a Model Predictive Control (MPC) system that accounts for powertrain signal delay and response lag using a First Order Plus Dead Time (FOPDT) model integrated with the vehicle’s longitudinal dynamics. The second stage employs lookup tables for the drive motor and brake system to convert control signals into actual vehicle inputs, ensuring precise throttle/brake pedal values for the desired driving torque. The proposed controller was validated using the CarMaker simulator and real vehicle tests with a Hyundai IONIQ5. In real vehicle tests, the proposed controller achieved a mean speed error of 0.54 km/h, outperforming conventional PID and standard MPC methods that do not account for powertrain delays. It also eliminated acceleration and deceleration overshoots and demonstrated real-time performance with an average computation time of 1.32 ms. Full article

This paper takes the dual-motor pure electric mining dump truck as the research object and designs a dual-motor rear-drive torque optimization allocation strategy in view of the problems such as the large load variation of the dump truck and the facts that the motor output torque cannot accurately express the driver’s dynamic intention and that the overall output efficiency of the dual motor is low. The strategy first divides the demand torque of the whole vehicle into two parts, the base torque and the compensation torque, which are determined by the accelerator pedal opening and the motor speed, and the compensation torque is fuzzy-controlled by taking the vehicle speed, the rate of change of the accelerator pedal opening, and the state of the battery charge as inputs. Subsequently, the dual-motor drive torque allocation is optimized using a particle swarm algorithm, with the objective of minimizing power loss in the dual motors. Furthermore, the energy-saving effect of the torque optimization allocation strategy proposed in this paper is compared with that of the traditional torque average allocation strategy under three working conditions: the driving conditions of Chinese dump trucks, the unloaded uphill movement of mining dump trucks, and the fully loaded downhill movement of mining dump trucks. The results show that the average efficiency of the dual-motor drive using the torque optimization allocation strategy is improved by 2.32%, 4.23%, and 2.24%, respectively, and battery energy savings are improved by 0.5%, 0.47%, and 0.24%, respectively. Full article

To further improve the braking energy recovery efficiency of battery electric vehicles and increase the range of the cars, this paper proposes a multi-mode switching braking energy recovery control strategy based on fuzzy control. The control strategy is divided into three modes: single-pedal energy recovery, coasting energy recovery, and conventional braking energy recovery. It takes the accelerator pedal and brake pedal opening as the switching conditions. It calculates the front and rear wheel braking ratio allocation coefficients and the motor braking ratio through fuzzy control to recover braking energy. The genetic algorithm (GA) is used to update the optimized affiliation function to optimize the motor braking allocation ratio through fuzzy control, and joint simulation is carried out based on the NEDC (New European Driving Cycle) and CLTC-P (China Light-duty Vehicle Test Cycle for Passenger vehicles) cycle conditions. The results show that the multi-mode braking energy recovery control strategy proposed in this paper improves the energy recovery rate and range contribution rate by 4% and 9.6%, respectively, and increases the range by 22.5 km under NEDC cycle conditions. It also improves the energy recovery rate and range contribution rate by 8.7% and 5.5%, respectively, and increases the range by 13 km under CLTC-P cycle conditions, which can effectively improve the energy recovery efficiency of the vehicle and increase the range of battery electric vehicles. Full article

With the recent advances in autonomous vehicles, there is an increasing need for sensors that can help monitor tire–road conditions and the forces that are applied to the tire. The footprint area of a tire that makes direct contact with the road surface, known as the contact patch, is a key parameter for determining a vehicle’s effectiveness in accelerating, braking, and steering at various velocities. Road unevenness from features such as potholes and cracks results in large fluctuations in the contact patch surface area. Such conditions can eventually require the driver to perform driving maneuvers unorthodox to normal traffic patterns, such as excessive pedal depressions or large steering inputs, which can escalate to hazards such as the loss of control or impact. The integration of sensors into the inner liner of a tire has proven to be a promising method for extracting real-time tire-to-road contact patch interface data. In this research, a tire model is developed using Abaqus/CAE and analyzed using Abaqus/Explicit to study the nonlinear behavior of a rolling tire. Strain variations are investigated at the contact patch in three major longitudinal slip driving scenarios, including acceleration, braking, and free-rolling. Multiple vertical loading conditions on the tire are applied and studied. An intelligent tire prototype called KU-iTire is developed and tested to validate the strain results obtained from the simulations. Similar operating and loading conditions are applied to the physical prototype and the simulation model such that valid comparisons can be made. The experimental investigation focuses on the effectiveness of providing usable and reliable tire-to-road contact patch strain variation data under several longitudinal slip operating conditions. In this research, a correlation between FEA and experimental testing was observed between strain shape for free-rolling, acceleration, and braking conditions. A relationship between peak longitudinal strain and vertical load in free-rolling driving conditions was also observed and a correlation was observed between FEA and physical testing. Full article

In this work we propose proof-of-concept methods to detect malfunctions of the braking system in passenger vehicles. In particular, we investigate the problem of detecting deformations of the brake disc based on data recorded by acceleration sensors mounted on the suspension of the vehicle. Our core hypothesis is that these signals contain vibrations caused by brake disc deformation. Since faults of this kind are typically monitored by the driver of the vehicle, the development of automatic fault-detection systems becomes more important with the rise of autonomous driving. In addition, the new brake boosters separate the brake pedal from the hydraulic system which results in less significant effects on the brake pedal force. Our paper offers two important contributions. Firstly, we provide a detailed description of our novel measurement scheme, the type and placement of the used sensors, signal acquisition and data characteristics. Then, in the second part of our paper we detail mathematically justified signal representations and different algorithms to distinguish between deformed and normal brake discs. For the proper understanding of the phenomenon, different brake discs were used with measured runout values. Since, in addition to brake disc deformation, the vibrations recorded by our accelerometers are nonlinearly dependent on a number of factors (such as the velocity, suspension, tire pressure, etc.), data-driven models are considered. Through experiments, we show that the proposed methods can be used to recognize faults in the braking system caused by brake disc deformation. Full article

An identification technique is proposed to create a relation between the accelerator pedal position and the corresponding driving moment. This step is beneficial to replace the complex physical model of the vehicle control unit, especially when the sufficient information needed to model certain functionalities of the vehicle control unit are unavailable. We utilized the nonlinear autoregressive exogenous model to regenerate the electric motor torque demand, given the accelerator pedal position, the motor’s angular speed, and the vehicle’s speed. This model proved to be extremely efficient in representing this highly complex relationship. The data employed for the identification process were chosen from an actual three-dimensional route with sudden changes of a dynamic nature in the driving mode, different speed limits, and elevations, as an attempt to thoroughly cover the driving moment scope based on the alternation of the given inputs. Analyzing the selected route data points showed the widespread coverage of the motor’s operational scope compared to a standard driving cycle. The training outcome revealed that linear modeling is inadequate for identifying the targeted system, and has a substantial estimation error. Adding the nonlinearity feature to the model led to an exceptionally high accuracy for the estimation and validation datasets. The main finding of this work is that the combined model from the nonlinear autoregressive exogenous and the sigmoid network enables the accurate modeling of highly nonlinear dynamic systems. Accordingly, the maximum absolute estimation error for the motor’s moment was less than 10 Nm during the real-world driving maneuver. The highest errors are found around the maximum motor’s moment. Finally, the model is validated with measurements from an actual field test maneuver. The identified model predicted the driving moment with a correlation of 0.994. Full article

The transportation sector remains a major contributor to greenhouse gas emissions. The understanding of energy-efficient driving behaviors and utilization of energy-efficient driving strategies are essential to reduce vehicles’ fuel consumption. However, there is no comprehensive investigation into energy-efficient driving behaviors and strategies. Furthermore, many state-of-the-art AI models have been applied for the analysis of eco-friendly driving styles, but no overview is available. To fill the gap, this paper conducts a thorough literature review on ecological driving behaviors and styles, and analyzes the driving factors influencing energy consumption and state-of-the-art methodologies. With a thorough scoping review process, thirty-seven articles with full text were assessed, and the methodological and related data are compared. The results show that the factors that impact driving behaviors can be summarized into eleven features including speed, acceleration, deceleration, pedal, steering, gear, engine, distance, weather, traffic signal, and road parameters. This paper finds that supervised/unsupervised learning algorithms and reinforcement learning frameworks have been popularly used to model the vehicle’s energy consumption with multi-dimensional data. Furthermore, the literature shows that the driving data are collected from either simulators or real-world experiments, and the real-world data are mainly stored and transmitted by meters, controller area networks, onboard data services, smartphones, and additional sensors installed in the vehicle. Based on driving behavior factors, driver characteristics, and safety rules, this paper recommends nine energy-efficient driving styles including four guidelines for the drivers’ selection and adjustment of the vehicle parameters, three recommendations for the energy-efficient driving styles in different driving scenarios, and two subjective suggestions for different types of drivers and employers. Full article

In this paper, a special recurrent neural network (RNN) called Long Short-Term Memory (LSTM) is used to design a virtual load sensor that estimates the mass of heavy vehicles. The estimation algorithm consists of a two-layer LSTM network. The network estimates vehicle mass based on vehicle speed, longitudinal acceleration, engine speed, engine torque, and accelerator pedal position. The network is trained and tested with a data set collected in a high-fidelity simulation environment called Truckmaker. The training data are generated in acceleration maneuvers across a range of speeds, while the test data are obtained by simulating the vehicle in the Worldwide harmonized Light vehicles Test Cycle (WLTC). Preliminary results show that, with the proposed approach, heavy-vehicle mass can be estimated as accurately as commercial load sensors across a range of load mass as wide as four tons. Full article