Subjects -> TRANSPORTATION (Total: 214 journals)
    - AIR TRANSPORT (9 journals)
    - AUTOMOBILES (26 journals)
    - RAILROADS (10 journals)
    - ROADS AND TRAFFIC (9 journals)
    - SHIPS AND SHIPPING (43 journals)
    - TRANSPORTATION (117 journals)

AUTOMOBILES (26 journals)

Showing 1 - 26 of 26 Journals sorted alphabetically
ATZ - Automobiltechnische Zeitschrift     Hybrid Journal   (Followers: 7)
ATZ worldwide     Hybrid Journal   (Followers: 2)
ATZautotechnology     Hybrid Journal   (Followers: 1)
ATZelektronik     Hybrid Journal   (Followers: 2)
ATZelektronik worldwide     Hybrid Journal   (Followers: 1)
ATZextra     Hybrid Journal   (Followers: 1)
ATZextra worldwide     Hybrid Journal  
ATZproduktion     Hybrid Journal   (Followers: 1)
ATZproduktion worldwide     Hybrid Journal  
Auto Tech Review     Hybrid Journal  
Automotive Agenda     Hybrid Journal   (Followers: 1)
Automotive and Engine Technology     Hybrid Journal  
Automotive Experiences     Open Access  
Automotive Innovation     Hybrid Journal  
Bulletin of NTU - Dynamics and strength of machines     Open Access  
IEEE Transactions on Intelligent Vehicles     Hybrid Journal   (Followers: 2)
International Journal of Automotive Composites     Hybrid Journal   (Followers: 5)
International Journal of Automotive Science And Technology     Open Access   (Followers: 1)
International Journal of Automotive Technology     Hybrid Journal   (Followers: 4)
International Journal of Automotive Technology and Management     Hybrid Journal   (Followers: 5)
International Journal of Vehicle Performance     Hybrid Journal  
MECCA Journal of Middle European Construction and Design of Cars     Open Access  
MTZ - Motortechnische Zeitschrift     Hybrid Journal   (Followers: 2)
MTZ industrial     Hybrid Journal   (Followers: 2)
MTZ worldwide     Hybrid Journal  
Proceedings of the Institution of Mechanical Engineers Part D: Journal of Automobile Engineering     Hybrid Journal   (Followers: 14)
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Automotive Innovation
Number of Followers: 0  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2096-4250 - ISSN (Online) 2522-8765
Published by Springer-Verlag Homepage  [2468 journals]
  • Review and Perspectives on Human Emotion for Connected Automated Vehicles

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      Abstract: Abstract The progression toward automated driving and the latest advancement in vehicular networking have led to novel and natural human-vehicle-road systems, in which affective human-vehicle interaction is a crucial factor affecting the acceptance, safety, comfort, and traffic efficiency of connected and automated vehicles (CAVs). This development has inspired increasing interest in how to develop affective interaction framework for intelligent cockpit in CAVs. To enable affective human-vehicle interactions in CAVs, knowledge from multiple research areas is needed, including automotive engineering, transportation engineering, human–machine interaction, computer science, communication, as well as industrial engineering. However, there is currently no systematic survey considering the close relationship between human-vehicle-road and human emotion in the human-vehicle-road coupling process in the CAV context. To facilitate progress in this area, this paper provides a comprehensive literature survey on emotion-related studies from multi-aspects for better design of affective interaction in intelligent cockpit for CAVs. This paper discusses the multimodal expression of human emotions, investigates the human emotion experiment in driving, and particularly emphasizes previous knowledge on human emotion detection, regulation, as well as their applications in CAVs. The promising research perspectives are outlined for researchers and engineers from different research areas to develop CAVs with better acceptance, safety, comfort, and enjoyment for users.
      PubDate: 2024-02-01
       
  • Preface for Feature Topic on Human Driver Behaviours for Intelligent
           Vehicles

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      PubDate: 2024-01-19
       
  • Driver Steering Behaviour Modelling Based on Neuromuscular Dynamics and
           Multi-Task Time-Series Transformer

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      Abstract: Abstract Driver steering intention prediction provides an augmented solution to the design of an onboard collaboration mechanism between human driver and intelligent vehicle. In this study, a multi-task sequential learning framework is developed to predict future steering torques and steering postures based on upper limb neuromuscular electromyography signals. The joint representation learning for driving postures and steering intention provides an in-depth understanding and accurate modelling of driving steering behaviours. Regarding different testing scenarios, two driving modes, namely, both-hand and single-right-hand modes, are studied. For each driving mode, three different driving postures are further evaluated. Next, a multi-task time-series transformer network (MTS-Trans) is developed to predict the future steering torques and driving postures based on the multi-variate sequential input and the self-attention mechanism. To evaluate the multi-task learning performance and information-sharing characteristics within the network, four distinct two-branch network architectures are evaluated. Empirical validation is conducted through a driving simulator-based experiment, encompassing 21 participants. The proposed model achieves accurate prediction results on future steering torque prediction as well as driving posture recognition for both two-hand and single-hand driving modes. These findings hold significant promise for the advancement of driver steering assistance systems, fostering mutual comprehension and synergy between human drivers and intelligent vehicles.
      PubDate: 2024-01-11
       
  • Curve Trajectory Model for Human Preferred Path Planning of Automated
           Vehicles

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      Abstract: Abstract Automated driving systems are often used for lane keeping tasks. By these systems, a local path is planned ahead of the vehicle. However, these paths are often found unnatural by human drivers. In response to this, this paper proposes a linear driver model, which can calculate node points reflective of human driver preferences and based on these node points a human driver preferred motion path can be designed for autonomous driving. The model input is the road curvature, effectively harnessed through a self-developed Euler-curve-based curve fitting algorithm. A comprehensive case study is undertaken to empirically validate the efficacy of the proposed model, demonstrating its capacity to emulate the average behavioral patterns observed in human curve path selection. Statistical analyses further underscore the model's robustness, affirming the authenticity of the established relationships. This paradigm shift in trajectory planning holds promising implications for the seamless integration of autonomous driving systems with human driving preferences.
      PubDate: 2024-01-09
       
  • Mechanically Joined Extrusion Profiles for Battery Trays

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      Abstract: Abstract In the context of electromobility, ensuring the leak tightness of assemblies is of paramount importance, particularly in battery housings. Current battery housings, often featuring base assemblies crafted from extruded aluminum profiles, address the challenge of leak tightness at joints through methods like friction stir welding, a process known for its time and cost intensiveness. The aim of this study is to develop and implement a new type of extruded profile concept to produce tight base assemblies for battery housings by a longitudinal mechanical single stroke joining process. The geometry, the process and the properties of the aluminum profiles are investigated to get a joint that meets the tightness requirements and achieve high load-bearing capacities in agreement with the high homologation requirements set to vehicles with high-voltage systems. The joint is formed by means of a single stage press stroke, which eliminates the need for complex tool designs that are necessary for continuous joining (roll joining). Flat steel contact surfaces are used as joining tools. To evaluate the joint quality, force curves from the joining process are analyzed and the resulting joint geometries are assessed using micrographs. The resulting leak tightness of the linear joints is measured by a helium sniffer leak detector and the load-bearing capacities are investigated by shear lap and bending tests and fatigue strength test. The study also explores whether a difference in strength between the two joining partners has a positive effect on the joint properties.
      PubDate: 2024-01-07
       
  • Review of Electrical and Electronic Architectures for Autonomous Vehicles:
           Topologies, Networking and Simulators

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      Abstract: Abstract With the rapid development of autonomous vehicles, more and more functions and computing requirements have led to the continuous centralization in the topology of electrical and electronic (E/E) architectures. While certain Tier1 suppliers, such as BOSCH, have previously proposed a serial roadmap for E/E architecture development, implemented since 2015 with significant contributions to the automotive industry, lingering misconceptions and queries persist in actual engineering processes. Notably, there are concerns regarding the perspective of zone-oriented E/E architectures, characterized by zonal concentration, as successors to domain-oriented E/E architectures, known for functional concentration. Addressing these misconceptions and queries, this study introduces a novel parallel roadmap for E/E architecture development, concurrently evaluating domain-oriented and zone-oriented schemes. Furthermore, the study explores hybrid E/E architectures, amalgamating features from both paradigms. To align with the evolution of E/E architectures, networking technologies must adapt correspondingly. The networking mechanisms pivotal in E/E architecture design are comprehensively discussed. Additionally, the study delves into modeling and verification tools pertinent to E/E architecture topologies. In conclusion, the paper outlines existing challenges and unresolved queries in this domain.
      PubDate: 2024-01-06
       
  • Mode Switching and Consistency Control for Electric-Hydraulic Hybrid
           Steering System

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      Abstract: Abstract Electric-hydraulic hybrid power steering (E-HHPS) system, a novel device with multiple modes for commercial electric vehicles, is designed to realize both superior steering feel and high energy efficiency. However, inconsistent steering performance occurs in the mode-switching process due to different dynamic characteristics of electric and hydraulic components, which even threatens driving safety. In this paper, mode-switching strategy and dynamic compensation control method are proposed for the E-HHPS system to eliminate the inconsistency of steering feel, which comprehensively considers ideal assistance characteristics and energy consumption of the system. Then, the influence of disturbances on system stability is analyzed, and H∞ robust controller is employed to guarantee system robustness and stability. The experimental results demonstrate that the proposed strategy can provide a steering system with natural steering feel without apparent inconsistency and effectively minimize energy consumption.
      PubDate: 2024-01-06
       
  • Frequency and Reliability Analysis of Load-Bearing Composite Beams

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      Abstract: Abstract The increasing utilization of fiber-reinforced thermoplastics (FRTPs) as a substitute for metal in load-bearing structures poses challenges related to NVH issues arising from frequency variations and reliability concerns stemming from fiber dispersion within the resin matrix. In this study, the steel automobile seat beam serves as a benchmark for comparison. FRTP beams are designed and fabricated using two distinct processes: compression molding and injection over-molding. Subsequently, their modal frequency and reliability are meticulously analyzed. Experimental investigations are conducted to explore the influence of various factors, including the combination of laminates and ribs, as well as the stacking sequence of laminates, on the modal frequency. The findings reveal that the modal frequency and vibration mode are subject to alterations based on the fiber type, beam material, and laminate stacking sequence. Notably, in comparison to the steel benchmark, the first-order frequency of the FRTP beam in this study experiences a 6.59% increase while simultaneously achieving a weight reduction of 32.42%. To assess reliability, a comprehensive analysis is performed, considering a six-fold standard deviation. This analysis yields the permissible range of fluctuation for material elastic constants, bending performance, and frequency response. Encouragingly, the FRTP beams meet the required reliability criteria. These results provide valuable insights for comprehending the stiffness-dependent response and effectively controlling structural performance when implementing FRTP for weight reduction purposes.
      PubDate: 2024-01-05
       
  • LLTH-YOLOv5: A Real-Time Traffic Sign Detection Algorithm for Low-Light
           Scenes

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      Abstract: Abstract Traffic sign detection is a crucial task for autonomous driving systems. However, the performance of deep learning-based algorithms for traffic sign detection is highly affected by the illumination conditions of scenarios. While existing algorithms demonstrate high accuracy in well-lit environments, they suffer from low accuracy in low-light scenarios. This paper proposes an end-to-end framework, LLTH-YOLOv5, specifically tailored for traffic sign detection in low-light scenarios, which enhances the input images to improve the detection performance. The proposed framework comproses two stages: the low-light enhancement stage and the object detection stage. In the low-light enhancement stage, a lightweight low-light enhancement network is designed, which uses multiple non-reference loss functions for parameter learning, and enhances the image by pixel-level adjustment of the input image with high-order curves. In the object detection stage, BIFPN is introduced to replace the PANet of YOLOv5, while designing a transformer-based detection head to improve the accuracy of small target detection. Moreover, GhostDarkNet53 is utilized based on Ghost module to replace the backbone network of YOLOv5, thereby improving the real-time performance of the model. The experimental results show that the proposed method significantly improves the accuracy of traffic sign detection in low-light scenarios, while satisfying the real-time requirements of autonomous driving.
      PubDate: 2024-01-05
       
  • In-Vehicle Network Injection Attacks Detection Based on Feature Selection
           and Classification

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      Abstract: Abstract Detecting abnormal data generated from cyberattacks has emerged as a crucial approach for identifying security threats within in-vehicle networks. The transmission of information through in-vehicle networks needs to follow specific data formats and communication protocols regulations. Typically, statistical algorithms are employed to learn these variation rules and facilitate the identification of abnormal data. However, the effectiveness of anomaly detection outcomes often falls short when confronted with highly deceptive in-vehicle network attacks. In this study, seven representative classification algorithms are selected to detect common in-vehicle network attacks, and a comparative analysis is employed to identify the most suitable and favorable detection method. In consideration of the communication protocol characteristics of in-vehicle networks, an optimal convolutional neural network (CNN) detection algorithm is proposed that uses data field characteristics and classifier selection, and its comprehensive performance is tested. In addition, the concept of Hamming distance between two adjacent packets within the in-vehicle network is introduced, enabling the proposal of an enhanced CNN algorithm that achieves robust detection of challenging-to-identify abnormal data. This paper also presents the proposed CNN classification algorithm that effectively addresses the issue of high false negative rate (FNR) in abnormal data detection based on the timestamp feature of data packets. The experimental results validate the efficacy of the proposed abnormal data detection algorithm, highlighting its strong detection performance and its potential to provide an effective solution for safeguarding the security of in-vehicle network information.
      PubDate: 2024-01-05
       
  • Human–Machine Shared Lateral Control Strategy for Intelligent Vehicles
           Based on Human Driver Risk Perception Reliability

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      Abstract: Abstract Intelligent vehicle (IV) technology has developed rapidly in recent years. However, achieving fully unmanned driving still presents numerous challenges, which means that human drivers will continue to play a vital role in vehicle operation for the foreseeable future. Human–machine shared driving, involving cooperation between a human driver and an automated driving system (AVS), has been widely regarded as a necessary stage for the development of IVs. Focusing on IV driving safety, this study proposed a human–machine shared lateral control strategy (HSLCS) based on the reliability of driver risk perception. The HSLCS starts by identifying the effective areas of driver risk perception based on eye movements. It establishes an anisotropic driving risk field, which serves as the foundation for the AVS to assess risk levels. Building upon the cumulative and diminishing effects of risk perception, the proposed approach leverages the driver's risk perception effective area and converts the risk field into a representation aligned with the driver's perspective. Subsequently, it quantifies the reliability of the driver's risk perception by using area-matching rules. Finally, based on the driver’s risk perception reliability and differences in lateral driving operation between the human driver and the AVS, the dynamic distribution of driving authority is achieved through a fuzzy rule-based system, and the human–machine shared lateral control is completed by using model predictive control. The HSLCS was tested across various scenarios on a driver-in-the-loop test platform. The results show that the HSLCS can realize the synergy and complementarity of human and machine intelligence, effectively ensuring the safety of IV operation.
      PubDate: 2024-01-04
       
  • A Hierarchical LSTM-Based Vehicle Trajectory Prediction Method Considering
           Interaction Information

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      Abstract: Abstract Trajectory prediction is an essential component in autonomous driving systems, as it can forecast the future movements of surrounding vehicles, thereby enhancing the decision-making and planning capabilities of autonomous driving systems. Traditional models relying on constant acceleration and constant velocity often experience a reduction in prediction accuracy as the forecasted timeframe extends. This limitation makes it challenging to meet the demands for medium to long-term trajectory prediction. Conversely, data-driven models, particularly those based on Long Short-Term Memory (LSTM) neural networks, have demonstrated superior performance in medium to long-term trajectory prediction. Therefore, this study introduces a hierarchical LSTM-based method for vehicle trajectory prediction. Considering the difficulty of using a single LSTM model to predict trajectories for all driving intentions, the trajectory prediction task is decomposed into three sequential steps: driving intention prediction, lane change time prediction, and trajectory prediction. Furthermore, given that the driving intent and trajectory of a vehicle are always subject to the influence of the surrounding traffic flow, the predictive model proposed in this paper incorporates the interactional information of neighboring vehicle movements into the model input. The proposed method is trained and validated on the real vehicle trajectory dataset Next Generation Simulation. The results show that the proposed hierarchical LSTM method has a lower prediction error compared to the integral LSTM model.
      PubDate: 2024-01-04
       
  • Analysis and Optimization of Transient Mode Switching Behavior for Power
           Split Hybrid Electric Vehicle with Clutch Collaboration

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      Abstract: Abstract The power split hybrid electric vehicle (HEV) adopts a power coupling configuration featuring dual planetary gearsets and multiple clutches, enabling diverse operational modes through clutch engagement and disengagement. The multi-clutch configuration usually involves the collaboration of two clutches during the transient mode switching process, thereby substantially elevating control complexity. This study focuses on power split HEVs that integrate multi-clutch mechanisms and investigates how different clutch collaboration manners impact the characteristics of transient mode switching. The powertrain model for the power-split HEV is established utilizing matrix-based methodologies. Through the formulation of clutch torque curves and clutch collaboration models, this research systematically explores the effects of clutch engagement timing and the duration of clutch slipping state on transient mode switching behaviors. Building upon this analysis, an optimization problem for control parameters pertaining to the two collaborative clutches is formulated. The simulated annealing algorithm is employed to optimize these control parameters. Simulation results demonstrate that the clutch collaboration manners have a great influence on the transient mode switching performance. Compared with the pre-calibrated benchmark and the optimal solution derived by the genetic algorithm, the maximal longitudinal jerk and clutch slipping work during the transient mode switching process is reduced obviously with the optimal control parameters derived by the simulated annealing algorithm. The study provides valuable insights for the dynamic coordinated control of the power-split HEVs featuring complex clutch collaboration mechanisms.
      PubDate: 2023-12-22
       
  • Effect of Laser Processing Pattern on the Mechanical Properties of
           Aluminum Alloy Adhesive Joints

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      Abstract: Abstract Adhesive bonding is a promising joining technology for joining lightweight aluminum structures, offering advantages such as the absence of additional heat input, connection damage, and environmental pollution. To further enhance the strength of aluminum adhesive joints, this study investigates the influence of laser surface treatment on their mechanical properties. Specifically, the effect of laser processing patterns and their geometric parameters on aluminum alloy adhesive joints is examined. A fiber laser is used to process crater array and multi-groove pattern on A6061 aluminum surface. The impact of crater overlap ratio and groove distance on various aspects, including aluminum surface morphology, roughness (Sa), adhesive joints shear, tensile strength, and failure modes is discussed. Laser confocal microscope tests, water contact angle tests, lap shear tests, and cross tensile tests are employed to analyze these parameters. The results indicate that as the crater overlap ratio increases, the Sa value of the aluminum surface increases. Moreover, the shear strength of adhesive joints initially increases and then decreases, while the tensile strength consistently increases. On the other hand, an increase in groove distance leads to a decrease in Sa, as well as a reduction in both shear and tensile strength of adhesive joints. For shear loading conditions, mechanical interlocking is identified as one of the bonding mechanisms in aluminum adhesive joints featuring crater array and multi-groove patterns. The formation of interlocking structures is found to be influenced by the aluminum surface pattern and its associated parameters, as revealed through failure surface analysis. Specifically, adhesive and crater or groove interactions contribute to the formation of interlocking structures in specimens with a crater overlap ratio of − 60% or groove distances of 120, 180, 300, and 400 μm. Conversely, specimens with overlap ratios of 0%, 40%, and 60% exhibit interlocking structures formed by the adhesive and crater edge.
      PubDate: 2023-11-13
      DOI: 10.1007/s42154-023-00274-9
       
  • Genetic Algorithm-Based SOTIF Scenario Construction for Complex Traffic
           Flow

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      Abstract: Abstract The Safety of The Intended Functionality (SOTIF) challenge represents the triggering condition by elements of a specific scenario and exposes the function limitation of an autonomous vehicle (AV), which leads to hazards. As for operation-content-related features, the scenario is similar to AVs’ SOTIF research and development. Therefore, scenario generation is a significant topic for SOTIF verification and validation procedure, especially in the simulation testing of AVs. Thus, in this paper, a well-designed scenario architecture is first defined, with comprehensive scenario elements, to present SOTIF trigger conditions. Then, considering complex traffic disturbance as trigger conditions, a novel SOTIF scenario generation method is developed. An indicator, also known as Scenario Potential Risk, is defined as the combination of the safety control intensity and the prior collision probability. This indicator helps identify critical scenarios in the proposed method. In addition, the corresponding vehicle motion models are established for general straight roads, curved roads, and safety assessment areas. As for the traffic participants’ motion model, it is designed to construct the key dynamic events. To efficiently search for critical scenarios with the trigger of complex traffic flow, this scenario is encoded as genes and it is regenerated through selection, mutation, and crossover iteration processes, known as the Genetic Algorithm (GA). Experimental results show that the GA-based method could efficiently construct diverse and critical traffic scenarios, contributing to the construction of the SOTIF scenario library.
      PubDate: 2023-11-09
      DOI: 10.1007/s42154-023-00251-2
       
  • Towards Safe Autonomous Driving: Decision Making with Observation-Robust
           Reinforcement Learning

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      Abstract: Abstract Most real-world situations involve unavoidable measurement noises or perception errors which result in unsafe decision making or even casualty in autonomous driving. To address these issues and further improve safety, automated driving is required to be capable of handling perception uncertainties. Here, this paper presents an observation-robust reinforcement learning against observational uncertainties to realize safe decision making for autonomous vehicles. Specifically, an adversarial agent is trained online to generate optimal adversarial attacks on observations, which attempts to amplify the average variation distance on perturbed policies. In addition, an observation-robust actor-critic approach is developed to enable the agent to learn the optimal policies and ensure that the changes of the policies perturbed by optimal adversarial attacks remain within a certain bound. Lastly, the safe decision making scheme is evaluated on a lane change task under complex highway traffic scenarios. The results show that the developed approach can ensure autonomous driving performance, as well as the policy robustness against adversarial attacks on observations.
      PubDate: 2023-11-08
      DOI: 10.1007/s42154-023-00256-x
       
  • Optimal-Control-Based Eco-Driving Solution for Connected Battery Electric
           Vehicle on a Signalized Route

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      Abstract: Abstract Speed advisory systems have been used for connected vehicles to optimize energy consumption. However, for their practical utilization, presence of preceding vehicles and signals must be taken into account. Moreover, for Battery Electric Vehicles (BEVs), factors that deteriorate battery’s life cycle and discharging time must also be considered. This paper proposes an eco-driving control for connected BEV with traffic signals and other safety constraints. Traffic signals are considered as interior point constraints, while inter-vehicle distance with preceding vehicles, vehicle speed and battery charging/discharging limits, are considered as state safety constraints. Backward-forward simulator based Speed Guidance Model is applied to follow the optimized velocity under powertrain safety limitations. Effectiveness of the proposed methodology is tested on a 5.3-km route in Islamabad, Pakistan. Real traffic data using Simulation of Urban Mobility under different driving scenarios is considered. Using the proposed method, around 21% energy can be saved compared to the preceding vehicles that followed their random velocities under the same traffic and route conditions. This means the EV controlled by the proposed method can have longer driving range. Furthermore, the host BEV has crossed signals during their green time without collision with preceding vehicles. Low charging rates and terminal Depth of Discharge indicate less number of charging cycles, thus proving the usefulness of the proposed solution as battery’s lifesaving strategy.
      PubDate: 2023-11-02
      DOI: 10.1007/s42154-023-00255-y
       
  • A New Method for Estimating Lithium-Ion Battery State-of-Energy Based on
           Multi-timescale Filter

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      Abstract: Abstract Accurate estimation of the state-of-energy (SOE) in lithium-ion batteries is critical for optimal energy management and energy optimization in electric vehicles. However, the conventional recursive least squares (RLS) algorithm struggle to track changes in battery model parameters under dynamic conditions. To address this, a multi-timescale estimator is proposed. A variable forgetting factor RLS approach is used to determine the model parameters at a macro timescale, and the H infinity filter is utilized to estimate the SOE at a micro timescale. The proposed algorithm is verified and analyzed and shown to have accurate and robust identification of battery model parameters. Finally, experiments under dynamic cycles demonstrate that the proposed algorithm has a high level of accuracy for SOE estimation.
      PubDate: 2023-11-01
      DOI: 10.1007/s42154-023-00271-y
       
  • Fault Diagnosis of Proton Exchange Membrane Fuel Cell Based on Nonlinear
           Impedance Spectrum

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      Abstract: Abstract Electrochemical impedance spectroscopy (EIS) contributes to developing the fault diagnosis tools for fuel cells, which is of great significance in improving service life. The conventional impedance measurement techniques are limited to linear responses, failing to capture high-order harmonic responses. However, nonlinear electrochemical impedance analysis incorporates additional nonlinear information, enabling the resolution of such responses. This study proposes a novel multi-stage fault diagnosis method based on the nonlinear electrochemical impedance spectrum (NEIS). First, the impact of alternating current excitation amplitude on NEIS is analyzed. Then, a series of experiments are conducted to obtain NEIS data under various fault conditions, encompassing recoverable faults like flooding, drying, starvation, and their mixed faults, spanning different degrees of fault severity. Based on these experiments, both EIS and NEIS datasets are established, and principal component analysis is utilized to extract the main features, thereby reducing the dimensionality of the original data. Finally, a fault diagnosis model is constructed with the support vector machine (SVM) and random forest algorithms, with model hyperparameters optimized by a hybrid genetic particle swarm optimization (HGAPSO) algorithm. The results show that the diagnostic accuracy of NEIS is higher than that of traditional EIS, with the HGAPSO-SVM model achieving a 100% accurate diagnosis under the NEIS dateset and self-defined fault labels.
      PubDate: 2023-10-30
      DOI: 10.1007/s42154-023-00253-0
       
  • European Research Project’s Contributions to a Safer Automated Road
           Traffic

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      Abstract: Abstract Automated driving is poised to become a pivotal technology in the future automotive transportation. However, it is evident that the implementation of automated driving presents significant technical challenges. To accelerate the development and deployment of automated driving the European Commission initiated the research project L3Pilot in 2017. With a budget of 65 million Euros and the involvement of 13 car manufacturers, L3Pilot stands as the largest European project on automated driving (AD). This paper serves as a comprehensive account of BMW’s main activities in the L3Pilot project that ended in 2021. The research questions addressed in this project are related to the following topics: what are the guidelines for the development of AD' How do potential customers interact with AD' And what is the safety impact assessment of AD' The paper presents the findings related to all three research questions to contribute to the further development of automated driving. For this purpose together with other partners the Code of Practice of AD was defined as a guideline for the development of future AD systems. Related to the second question, BMW conducted tests with AD systems on motorways and in parking scenarios, with over 100 test subjects experiencing AD. The studies provide input and considerations for future AD systems. Finally, in the safety impact assessment, BMW investigated with other project partners the potential safety benefits of AD through simulation. The results show a potential to improve road safety. In conclusion, the exploration of all three research questions has led to a deeper understanding of SAE Level 3 AD.
      PubDate: 2023-10-20
      DOI: 10.1007/s42154-023-00250-3
       
 
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  Subjects -> TRANSPORTATION (Total: 214 journals)
    - AIR TRANSPORT (9 journals)
    - AUTOMOBILES (26 journals)
    - RAILROADS (10 journals)
    - ROADS AND TRAFFIC (9 journals)
    - SHIPS AND SHIPPING (43 journals)
    - TRANSPORTATION (117 journals)

AUTOMOBILES (26 journals)

Showing 1 - 26 of 26 Journals sorted alphabetically
ATZ - Automobiltechnische Zeitschrift     Hybrid Journal   (Followers: 7)
ATZ worldwide     Hybrid Journal   (Followers: 2)
ATZautotechnology     Hybrid Journal   (Followers: 1)
ATZelektronik     Hybrid Journal   (Followers: 2)
ATZelektronik worldwide     Hybrid Journal   (Followers: 1)
ATZextra     Hybrid Journal   (Followers: 1)
ATZextra worldwide     Hybrid Journal  
ATZproduktion     Hybrid Journal   (Followers: 1)
ATZproduktion worldwide     Hybrid Journal  
Auto Tech Review     Hybrid Journal  
Automotive Agenda     Hybrid Journal   (Followers: 1)
Automotive and Engine Technology     Hybrid Journal  
Automotive Experiences     Open Access  
Automotive Innovation     Hybrid Journal  
Bulletin of NTU - Dynamics and strength of machines     Open Access  
IEEE Transactions on Intelligent Vehicles     Hybrid Journal   (Followers: 2)
International Journal of Automotive Composites     Hybrid Journal   (Followers: 5)
International Journal of Automotive Science And Technology     Open Access   (Followers: 1)
International Journal of Automotive Technology     Hybrid Journal   (Followers: 4)
International Journal of Automotive Technology and Management     Hybrid Journal   (Followers: 5)
International Journal of Vehicle Performance     Hybrid Journal  
MECCA Journal of Middle European Construction and Design of Cars     Open Access  
MTZ - Motortechnische Zeitschrift     Hybrid Journal   (Followers: 2)
MTZ industrial     Hybrid Journal   (Followers: 2)
MTZ worldwide     Hybrid Journal  
Proceedings of the Institution of Mechanical Engineers Part D: Journal of Automobile Engineering     Hybrid Journal   (Followers: 14)
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JournalTOCs
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Heriot-Watt University
Edinburgh, EH14 4AS, UK
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