The positional deviation of the in-vehicle Automatic Emergency Call System (AECS) under collision conditions brings difficulties for Intelligent Connected Vehicles (ICVs) post rescue operations. Currently, there is a lack of analysis on system operating conditions during collisions in the reliability assessment methods for the Global Navigation Satellite System (GNSS) deployed in the AECS. Therefore, this paper establishes an in-vehicle collision environment simulation model for emergency calls to explore the influence of parameters such as temperature and vibration on Signal-Based In-Vehicle Emergency Call Systems. We also propose environmental limits applicable to comprehensive tests, which can objectively evaluate reliability and provide data support for the AECS bench test through a satellite-signal-based semi-physical simulation, which is subjected to a bench test under different operating conditions. The findings of this study demonstrate that the occurrence of random vibration and impact stress, induced by vibration, exerts considerable disruptive effects on positional signal data during collisions. Consequently, it leads to substantial interference with the accurate detection of post-collision satellite positioning information. When the simulation operates under a 2.4 gRMS vibration condition, the maximum phase noise error in the positioning system is 8.95%, which does not meet the test accuracy requirements. On the other hand, the semi-simulation system is less affected by temperature changes, and at the maximum allowable temperature difference of the equipment, the maximum phase noise error in the simulated signal is 2.12%. Therefore, based on the influence of phase noise variation on the accuracy of the satellite signal simulation, necessary environmental conditions for the test are obtained, including a temperature that is consistent with the maximum operating temperature of the vector generator and a vibration power spectral density (PSD) lower than 1.2 gRMS.
{"title":"Using Case and Error Analysis on Inspection Methods of Modeling Platforms for Automatic Emergency Call Systems Based on Generated Satellite Signals","authors":"Yining Fu, Xindong Ni, Jingxuan Yang, Bingjian Wang, Zhe Fang","doi":"10.3390/vehicles5040071","DOIUrl":"https://doi.org/10.3390/vehicles5040071","url":null,"abstract":"The positional deviation of the in-vehicle Automatic Emergency Call System (AECS) under collision conditions brings difficulties for Intelligent Connected Vehicles (ICVs) post rescue operations. Currently, there is a lack of analysis on system operating conditions during collisions in the reliability assessment methods for the Global Navigation Satellite System (GNSS) deployed in the AECS. Therefore, this paper establishes an in-vehicle collision environment simulation model for emergency calls to explore the influence of parameters such as temperature and vibration on Signal-Based In-Vehicle Emergency Call Systems. We also propose environmental limits applicable to comprehensive tests, which can objectively evaluate reliability and provide data support for the AECS bench test through a satellite-signal-based semi-physical simulation, which is subjected to a bench test under different operating conditions. The findings of this study demonstrate that the occurrence of random vibration and impact stress, induced by vibration, exerts considerable disruptive effects on positional signal data during collisions. Consequently, it leads to substantial interference with the accurate detection of post-collision satellite positioning information. When the simulation operates under a 2.4 gRMS vibration condition, the maximum phase noise error in the positioning system is 8.95%, which does not meet the test accuracy requirements. On the other hand, the semi-simulation system is less affected by temperature changes, and at the maximum allowable temperature difference of the equipment, the maximum phase noise error in the simulated signal is 2.12%. Therefore, based on the influence of phase noise variation on the accuracy of the satellite signal simulation, necessary environmental conditions for the test are obtained, including a temperature that is consistent with the maximum operating temperature of the vector generator and a vibration power spectral density (PSD) lower than 1.2 gRMS.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135459222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Detecting drivers’ cognitive states poses a substantial challenge. In this context, cognitive driving anomalies have generally been regarded as stochastic disturbances. To the best of the author’s knowledge, existing safety studies in the realm of human Driving Anomaly Detection (DAD) utilizing vehicle trajectories have predominantly been conducted at an aggregate level, relying on data aggregated from multiple drivers or vehicles. However, to gain a more nuanced understanding of driving behavior at the individual level, a more detailed and granular approach is essential. To bridge this gap, we developed a Data Anomaly Detection (DAD) model designed to assess a driver’s cognitive abnormal driving status at the individual level, relying solely on Basic Safety Message (BSM) data. Our DAD model comprises both online and offline components, each of which analyzes historical and real-time Basic Safety Messages (BSMs) sourced from connected vehicles (CVs). The training data for the DAD model consist of historical BSMs collected from a specific CV over the course of a month, while the testing data comprise real-time BSMs collected at the scene. By shifting our focus from aggregate-level analysis to individual-level analysis, we believe that the DAD model can significantly contribute to a more comprehensive comprehension of driving behavior. Furthermore, when combined with a Conflict Identification (CIM) model, the DAD model has the potential to enhance the effectiveness of Advanced Driver Assistance Systems (ADAS), particularly in terms of crash avoidance capabilities. It is important to note that this paper is part of our broader research initiative titled “Automatic Safety Diagnosis in the Connected Vehicle Environment”, which has received funding from the Southeastern Transportation Research, Innovation, Development, and Education Center.
{"title":"Adaptive Individual-Level Cognitive Driving Anomaly Detection Model Exclusively Using BSMs","authors":"Di Wu, Shuang Z. Tu, Robert W. Whalin, Li Zhang","doi":"10.3390/vehicles5040070","DOIUrl":"https://doi.org/10.3390/vehicles5040070","url":null,"abstract":"Detecting drivers’ cognitive states poses a substantial challenge. In this context, cognitive driving anomalies have generally been regarded as stochastic disturbances. To the best of the author’s knowledge, existing safety studies in the realm of human Driving Anomaly Detection (DAD) utilizing vehicle trajectories have predominantly been conducted at an aggregate level, relying on data aggregated from multiple drivers or vehicles. However, to gain a more nuanced understanding of driving behavior at the individual level, a more detailed and granular approach is essential. To bridge this gap, we developed a Data Anomaly Detection (DAD) model designed to assess a driver’s cognitive abnormal driving status at the individual level, relying solely on Basic Safety Message (BSM) data. Our DAD model comprises both online and offline components, each of which analyzes historical and real-time Basic Safety Messages (BSMs) sourced from connected vehicles (CVs). The training data for the DAD model consist of historical BSMs collected from a specific CV over the course of a month, while the testing data comprise real-time BSMs collected at the scene. By shifting our focus from aggregate-level analysis to individual-level analysis, we believe that the DAD model can significantly contribute to a more comprehensive comprehension of driving behavior. Furthermore, when combined with a Conflict Identification (CIM) model, the DAD model has the potential to enhance the effectiveness of Advanced Driver Assistance Systems (ADAS), particularly in terms of crash avoidance capabilities. It is important to note that this paper is part of our broader research initiative titled “Automatic Safety Diagnosis in the Connected Vehicle Environment”, which has received funding from the Southeastern Transportation Research, Innovation, Development, and Education Center.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134960050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Simon Unger, Markus Heinrich, Dirk Scheuermann, Stefan Katzenbeisser, Max Schubert, Leon Hagemann, Lukas Iffländer
The railway industry—traditionally a conservative industry with low adaption speed for innovation—is currently entering its digitization phase. The sector faces a challenge in integrating new technologies and approaches into the employed—often safety-critical—systems. Keeping the systems secure while conforming to the demanding safety norms creates previously unknown problems. In the last decades, the number of attacks on the railway system has increased. Furthermore, with standardized digital technologies, the attack surface will keep growing. Therefore, in this work, we look into the foreseeable future of the railway system and present 21 likely use cases. We analyze these use cases regarding possible threats, rate the severity of these threats, and deduce and rate necessary countermeasures. To this end, we model these use cases and the corresponding threats and countermeasures using Attack Graphs. We use a graphical solution for the risk and security analysis due to advantages over other methods, i.e., table-based solutions, like simplified presentation and an easier understanding of relationships, dependencies, and interactions between various elements. From these Attack Graphs, we extracted 14 commonly recurring attack strategies. After analyzing 49 countermeasures regarding their current maturity and further research and standardization demands, we identified 21 in need of further investigation. This implies that 21 necessary countermeasures to secure these future use cases require further research to apply to railway systems or require standardization. These results will help researchers focus on the necessary research and standardization and railway operators to ensure the security of their systems.
{"title":"Securing the Future Railway System: Technology Forecast, Security Measures, and Research Demands","authors":"Simon Unger, Markus Heinrich, Dirk Scheuermann, Stefan Katzenbeisser, Max Schubert, Leon Hagemann, Lukas Iffländer","doi":"10.3390/vehicles5040069","DOIUrl":"https://doi.org/10.3390/vehicles5040069","url":null,"abstract":"The railway industry—traditionally a conservative industry with low adaption speed for innovation—is currently entering its digitization phase. The sector faces a challenge in integrating new technologies and approaches into the employed—often safety-critical—systems. Keeping the systems secure while conforming to the demanding safety norms creates previously unknown problems. In the last decades, the number of attacks on the railway system has increased. Furthermore, with standardized digital technologies, the attack surface will keep growing. Therefore, in this work, we look into the foreseeable future of the railway system and present 21 likely use cases. We analyze these use cases regarding possible threats, rate the severity of these threats, and deduce and rate necessary countermeasures. To this end, we model these use cases and the corresponding threats and countermeasures using Attack Graphs. We use a graphical solution for the risk and security analysis due to advantages over other methods, i.e., table-based solutions, like simplified presentation and an easier understanding of relationships, dependencies, and interactions between various elements. From these Attack Graphs, we extracted 14 commonly recurring attack strategies. After analyzing 49 countermeasures regarding their current maturity and further research and standardization demands, we identified 21 in need of further investigation. This implies that 21 necessary countermeasures to secure these future use cases require further research to apply to railway systems or require standardization. These results will help researchers focus on the necessary research and standardization and railway operators to ensure the security of their systems.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135863326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kevin Steinbach, Dominik Lechler, Peter Kraemer, Iris Groß, Dirk Reith
This paper presents a novel approach to address noise, vibration, and harshness (NVH) issues in electrically assisted bicycles (e-bikes) caused by the drive unit. By investigating and optimising the structural dynamics during early product development, NVH can decisively be improved and valuable resources can be saved, emphasising its significance for enhancing riding performance. The paper offers a comprehensive analysis of the e-bike drive unit’s mechanical interactions among relevant components, culminating—to the best of our knowledge—in the development of the first high-fidelity model of an entire e-bike drive unit. The proposed model uses the principles of elastic multi body dynamics (eMBD) to elucidate the structural dynamics in dynamic-transient calculations. Comparing power spectra between measured and simulated motion variables validates the chosen model assumptions. The measurements of physical samples utilise accelerometers, contactless laser Doppler vibrometry (LDV) and various test arrangements, which are replicated in simulations and provide accessibility to measure vibrations onto rotating shafts and stationary structures. In summary, this integrated system-level approach can serve as a viable starting point for comprehending and managing the NVH behaviour of e-bikes.
{"title":"A Novel Approach to Predict the Structural Dynamics of E-Bike Drive Units by Innovative Integration of Elastic Multi-Body-Dynamics","authors":"Kevin Steinbach, Dominik Lechler, Peter Kraemer, Iris Groß, Dirk Reith","doi":"10.3390/vehicles5040068","DOIUrl":"https://doi.org/10.3390/vehicles5040068","url":null,"abstract":"This paper presents a novel approach to address noise, vibration, and harshness (NVH) issues in electrically assisted bicycles (e-bikes) caused by the drive unit. By investigating and optimising the structural dynamics during early product development, NVH can decisively be improved and valuable resources can be saved, emphasising its significance for enhancing riding performance. The paper offers a comprehensive analysis of the e-bike drive unit’s mechanical interactions among relevant components, culminating—to the best of our knowledge—in the development of the first high-fidelity model of an entire e-bike drive unit. The proposed model uses the principles of elastic multi body dynamics (eMBD) to elucidate the structural dynamics in dynamic-transient calculations. Comparing power spectra between measured and simulated motion variables validates the chosen model assumptions. The measurements of physical samples utilise accelerometers, contactless laser Doppler vibrometry (LDV) and various test arrangements, which are replicated in simulations and provide accessibility to measure vibrations onto rotating shafts and stationary structures. In summary, this integrated system-level approach can serve as a viable starting point for comprehending and managing the NVH behaviour of e-bikes.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135966929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abel Ortego, Sofia Russo, Marta Iglesias-Émbil, Alicia Valero, Ricardo Magdalena
Light-duty vehicles are increasingly incorporating plastic materials to reduce production costs and achieve lightweight designs. On average, a conventional car utilizes over 200 kg of plastic, comprising more than 23 different types, which often present challenges for recycling due to their incompatibility. Consequently, the focus on plastic recycling in end-of-life vehicles has intensified. This study aims to analyze critical car parts based on the plastics used, employing a novel thermodynamic approach that examines the embodied exergy (EE) of different plastics. Six vehicles from various segments, years, and equipment levels were assessed to understand their plastic compositions. The findings reveal that, on average, a vehicle contains 222 kg of plastic, accounting for 17.7% of its total weight. Among these plastics, 47.5% (105 kg) are utilized in car parts weighing over 1 kg, with plastics comprising over 80% of the part’s weight. The identified critical car parts include the front door trim panel, front and rear covers, fuel tank, floor covering, front lighting, dashboard, rear door trim panel, plastic front end, backrest pad, door trim panel pocket, plastic foam rear seat, rear lighting, window guide, molded headliner, bulkhead sound insulation, foam seat part, and wheel trim. Regarding their contribution to EE, the plastics with the highest shares are polypropylene—PP (24.5%), polypropylene and ethylene blends—E/P (20.3%), and polyurethane- PU (15.3%). Understanding the criticality of these car parts and their associated plastics enables targeted efforts in design, material selection, and end-of-life management to enhance recycling and promote circularity within the automotive industry.
轻型汽车越来越多地采用塑料材料，以降低生产成本，实现轻量化设计。平均而言，一辆传统汽车使用超过200公斤的塑料，包括超过23种不同类型的塑料，由于它们的不兼容性，这些塑料通常给回收带来挑战。因此，对报废车辆塑料回收的关注已经加强。本研究的目的是分析关键的汽车零部件基于所使用的塑料，采用一种新的热力学方法，检查不同塑料的具体火用(EE)。研究人员对六辆不同车型、不同年份和不同装备水平的汽车进行了评估，以了解它们的塑料成分。调查结果显示，一辆汽车平均含有222公斤塑料，占其总重量的17.7%。在这些塑料中，47.5%(105公斤)用于重量超过1公斤的汽车零件，塑料占零件重量的80%以上。确定的关键汽车部件包括前门饰板、前后盖、油箱、地板覆盖物、前照明灯、仪表板、后门饰板、塑料前端、靠背垫、门饰板口袋、塑料泡沫后座、后照明灯、车窗导板、成型头罩、隔舱隔音、泡沫座椅部件和车轮饰板。就其对EE的贡献而言，份额最高的塑料是聚丙烯- pp(24.5%)，聚丙烯和乙烯共混物- e /P(20.3%)和聚氨酯- PU(15.3%)。了解这些汽车零部件及其相关塑料的重要性，可以在设计、材料选择和报废管理方面做出有针对性的努力，从而加强回收利用，促进汽车行业的循环利用。
{"title":"Exergy Assessment of Plastic Car Parts","authors":"Abel Ortego, Sofia Russo, Marta Iglesias-Émbil, Alicia Valero, Ricardo Magdalena","doi":"10.3390/vehicles5030067","DOIUrl":"https://doi.org/10.3390/vehicles5030067","url":null,"abstract":"Light-duty vehicles are increasingly incorporating plastic materials to reduce production costs and achieve lightweight designs. On average, a conventional car utilizes over 200 kg of plastic, comprising more than 23 different types, which often present challenges for recycling due to their incompatibility. Consequently, the focus on plastic recycling in end-of-life vehicles has intensified. This study aims to analyze critical car parts based on the plastics used, employing a novel thermodynamic approach that examines the embodied exergy (EE) of different plastics. Six vehicles from various segments, years, and equipment levels were assessed to understand their plastic compositions. The findings reveal that, on average, a vehicle contains 222 kg of plastic, accounting for 17.7% of its total weight. Among these plastics, 47.5% (105 kg) are utilized in car parts weighing over 1 kg, with plastics comprising over 80% of the part’s weight. The identified critical car parts include the front door trim panel, front and rear covers, fuel tank, floor covering, front lighting, dashboard, rear door trim panel, plastic front end, backrest pad, door trim panel pocket, plastic foam rear seat, rear lighting, window guide, molded headliner, bulkhead sound insulation, foam seat part, and wheel trim. Regarding their contribution to EE, the plastics with the highest shares are polypropylene—PP (24.5%), polypropylene and ethylene blends—E/P (20.3%), and polyurethane- PU (15.3%). Understanding the criticality of these car parts and their associated plastics enables targeted efforts in design, material selection, and end-of-life management to enhance recycling and promote circularity within the automotive industry.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136236424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research focused on the rubber bushings of the rear sub-frame in an electric vehicle. A dynamic model was developed to represent the bushing, incorporating an elastic element, a frictional element, and a viscoelastic element arranged in series using a fractional-order Maxwell and a Kelvin–Voigt model. To identify the parameters of the bushing model, an improved adaptive chaotic particle swarm optimization algorithm was employed, in conjunction with dynamic stiffness test data obtained at an amplitude of 0.2 mm. The test data obtained at different amplitudes (0.2 mm, 0.3 mm, 0.5 mm, and 1 mm) were fitted to the model, resulting in fitting errors of 1.13%, 4.07%, 4.42%, and 28.82%, respectively, when compared to the corresponding test data in order to enhance the accuracy of the model fitting; the Sobol sensitivity analysis method was utilized to analyze the parameter sensitivity of the model. Following the analysis, the parameters α, β, and k2, which exhibited high sensitivity, were re-identified. This re-identification process led to a reduction in the fitting error at the 1 mm amplitude to 7.45%. The improved accuracy of the model plays a crucial role in enhancing the simulation accuracy of design of experiments (DOE) analysis and verifying the vehicle’s performance under various conditions, taking into account the influence of the bushing.
{"title":"Parameter Identification and Dynamic Characteristic Research of a Fractional Viscoelastic Model for Sub-Frame Bushing","authors":"Bao Chen, Lunyang Chen, Feng Zhou, Liang Cao, Shengxiang Guo, Zehao Huang","doi":"10.3390/vehicles5030066","DOIUrl":"https://doi.org/10.3390/vehicles5030066","url":null,"abstract":"This research focused on the rubber bushings of the rear sub-frame in an electric vehicle. A dynamic model was developed to represent the bushing, incorporating an elastic element, a frictional element, and a viscoelastic element arranged in series using a fractional-order Maxwell and a Kelvin–Voigt model. To identify the parameters of the bushing model, an improved adaptive chaotic particle swarm optimization algorithm was employed, in conjunction with dynamic stiffness test data obtained at an amplitude of 0.2 mm. The test data obtained at different amplitudes (0.2 mm, 0.3 mm, 0.5 mm, and 1 mm) were fitted to the model, resulting in fitting errors of 1.13%, 4.07%, 4.42%, and 28.82%, respectively, when compared to the corresponding test data in order to enhance the accuracy of the model fitting; the Sobol sensitivity analysis method was utilized to analyze the parameter sensitivity of the model. Following the analysis, the parameters α, β, and k2, which exhibited high sensitivity, were re-identified. This re-identification process led to a reduction in the fitting error at the 1 mm amplitude to 7.45%. The improved accuracy of the model plays a crucial role in enhancing the simulation accuracy of design of experiments (DOE) analysis and verifying the vehicle’s performance under various conditions, taking into account the influence of the bushing.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135205931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A review of past, current, and emerging electric vehicle (EV) propulsion system technologies and their integration is the focus of this paper, namely, the matching of electric motor (EM) and transmission (TRM) to meet basic requirements and performance targets. The fundaments of EM and TRM matching from a tractive effort and a vehicle dynamics perspective are provided as an introductory context to available or near-production propulsion system products available from OEM and Tier 1 suppliers. Engineering data and details regarding EM and TRM combinations are detailed with a specific focus on volumetric and mass density. Evolutionary trends in EM and TRM technologies have been highlighted and summarized through current and emerging products. The paper includes an overview of the initial EV propulsion system’s sizing and selection for a set of simple requirements that are provided through an examination of three light-duty EV applications. An enterprise approach to developing electrified propulsion modules with suitable applicability to a range of light-duty EVs from compact cars to full-size trucks concludes the paper.
{"title":"Electric Motor and Transmission Integration for Light-Duty Electric Vehicles: A 2023 Benchmarking Perspective and Component Sizing for a Fleet Approach","authors":"Darrell Robinette","doi":"10.3390/vehicles5030065","DOIUrl":"https://doi.org/10.3390/vehicles5030065","url":null,"abstract":"A review of past, current, and emerging electric vehicle (EV) propulsion system technologies and their integration is the focus of this paper, namely, the matching of electric motor (EM) and transmission (TRM) to meet basic requirements and performance targets. The fundaments of EM and TRM matching from a tractive effort and a vehicle dynamics perspective are provided as an introductory context to available or near-production propulsion system products available from OEM and Tier 1 suppliers. Engineering data and details regarding EM and TRM combinations are detailed with a specific focus on volumetric and mass density. Evolutionary trends in EM and TRM technologies have been highlighted and summarized through current and emerging products. The paper includes an overview of the initial EV propulsion system’s sizing and selection for a set of simple requirements that are provided through an examination of three light-duty EV applications. An enterprise approach to developing electrified propulsion modules with suitable applicability to a range of light-duty EVs from compact cars to full-size trucks concludes the paper.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134913572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper explores the use of driver-in-the-loop simulations to detect personalized driving styles in autonomous vehicles. The driving simulator used in this study is modular and adaptable, allowing for the testing and validation of control and data-collecting systems, as well as the incorporation and proof of car models. The selected scenario is a double lane change maneuver to overtake a stationary obstacle at a relatively high speed. The user’s behavior was recorded, and lateral accelerations during the maneuver were used as criteria to compare the user-driven vehicle and the autonomous one. The tuning parameters of the lateral and longitudinal controllers were modified to obtain different lateral accelerations of the autonomous vehicle. A neural network was developed to find the combination of the two controllers’ tuning parameters to match the driver’s lateral accelerations in the same double lane change overtaking action. The results are promising, and this study suggests that driver-in-the-loop simulations can help increase autonomous vehicles’ safety while preserving individual driving styles. This could result in creating more individualized and secure autonomous driving systems that consider the preferences and behavior of the driver.
{"title":"Personalized Driving Styles in Safety-Critical Scenarios for Autonomous Vehicles: An Approach Using Driver-in-the-Loop Simulations","authors":"Ioana-Diana Buzdugan, Silviu Butnariu, Ioana-Alexandra Roșu, Andrei-Cristian Pridie, Csaba Antonya","doi":"10.3390/vehicles5030064","DOIUrl":"https://doi.org/10.3390/vehicles5030064","url":null,"abstract":"This paper explores the use of driver-in-the-loop simulations to detect personalized driving styles in autonomous vehicles. The driving simulator used in this study is modular and adaptable, allowing for the testing and validation of control and data-collecting systems, as well as the incorporation and proof of car models. The selected scenario is a double lane change maneuver to overtake a stationary obstacle at a relatively high speed. The user’s behavior was recorded, and lateral accelerations during the maneuver were used as criteria to compare the user-driven vehicle and the autonomous one. The tuning parameters of the lateral and longitudinal controllers were modified to obtain different lateral accelerations of the autonomous vehicle. A neural network was developed to find the combination of the two controllers’ tuning parameters to match the driver’s lateral accelerations in the same double lane change overtaking action. The results are promising, and this study suggests that driver-in-the-loop simulations can help increase autonomous vehicles’ safety while preserving individual driving styles. This could result in creating more individualized and secure autonomous driving systems that consider the preferences and behavior of the driver.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135827034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Currently, in the course of the German mobility transition, an increasing number of disused rail lines are already being or intended to be reactivated in order to increase capacities, decrease transport-related emissions and reconnect rural areas to passenger rail services, thus creating a more comprehensive rail service. However, the use of state-of-the-art regional railcars on old rural infrastructure often leads to problems since they are often worn out and do not meet today’s technical standards. This applies, for example, to the axle loads and dimensions of the vehicles but also to operational aspects, such as the vehicle’s passenger capacity and accessibility. First, this work gives an overview of the available rolling stock and the given infrastructure, as well as an analysis of the (system) interfaces. Subsequently, various challenges for the re-connection of peripheral areas to the rail network were identified through data research and comparison of the vehicle and infrastructure parameters. In addition, the requirements related to possible autonomous operation and the related absence of the driver and crew personnel in the vehicle, which require new solutions in terms of safety, were taken into consideration. Orientation of future rolling stock generations towards the existing infrastructure and the required transport needs, including lower axle loads, accessibility and smaller capacities, can contribute to the economic operation of low-capacity lines and bring more passengers to public transport.
{"title":"Challenges in the (Re-)Connection of Peripheral Areas to the Rail Network from a Rolling Stock Perspective: The Case of Germany","authors":"Benedikt Hertel, Johannes Pagenkopf, Jens König","doi":"10.3390/vehicles5030063","DOIUrl":"https://doi.org/10.3390/vehicles5030063","url":null,"abstract":"Currently, in the course of the German mobility transition, an increasing number of disused rail lines are already being or intended to be reactivated in order to increase capacities, decrease transport-related emissions and reconnect rural areas to passenger rail services, thus creating a more comprehensive rail service. However, the use of state-of-the-art regional railcars on old rural infrastructure often leads to problems since they are often worn out and do not meet today’s technical standards. This applies, for example, to the axle loads and dimensions of the vehicles but also to operational aspects, such as the vehicle’s passenger capacity and accessibility. First, this work gives an overview of the available rolling stock and the given infrastructure, as well as an analysis of the (system) interfaces. Subsequently, various challenges for the re-connection of peripheral areas to the rail network were identified through data research and comparison of the vehicle and infrastructure parameters. In addition, the requirements related to possible autonomous operation and the related absence of the driver and crew personnel in the vehicle, which require new solutions in terms of safety, were taken into consideration. Orientation of future rolling stock generations towards the existing infrastructure and the required transport needs, including lower axle loads, accessibility and smaller capacities, can contribute to the economic operation of low-capacity lines and bring more passengers to public transport.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136192958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Today’s vehicle powertrains, especially in cars and vans, have to meet increasingly stringent type approval standards [...]
今天的车辆动力系统，特别是轿车和货车，必须满足越来越严格的型式认证标准[…]
{"title":"Development Trends in Vehicle Propulsion Sources—A Short Review","authors":"D. Szpica, B. Ashok, Hasan Köten","doi":"10.3390/vehicles5030062","DOIUrl":"https://doi.org/10.3390/vehicles5030062","url":null,"abstract":"Today’s vehicle powertrains, especially in cars and vans, have to meet increasingly stringent type approval standards [...]","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75618708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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