2023 IEEE International Workshop on Metrology for Automotive (MetroAutomotive)最新文献_第4页

An Adaptive TinyML Unsupervised Online Learning Algorithm for Driver Behavior Analysis 一种自适应TinyML无监督在线学习算法用于驾驶员行为分析

2023 IEEE International Workshop on Metrology for Automotive (MetroAutomotive) Pub Date : 2023-06-28 DOI: 10.1109/MetroAutomotive57488.2023.10219125

Marianne Silva, Thaís Medeiros, Mariana Azevedo, Morsinaldo Medeiros, Mikael P. B. Themoteo, Tatiane Gois, I. Silva, Dan Costa

{"title":"An Adaptive TinyML Unsupervised Online Learning Algorithm for Driver Behavior Analysis","authors":"Marianne Silva, Thaís Medeiros, Mariana Azevedo, Morsinaldo Medeiros, Mikael P. B. Themoteo, Tatiane Gois, I. Silva, Dan Costa","doi":"10.1109/MetroAutomotive57488.2023.10219125","DOIUrl":"https://doi.org/10.1109/MetroAutomotive57488.2023.10219125","url":null,"abstract":"The Internet of Things (IoT) has significantly impacted various industries, particularly the automotive sector, due to the growing integration of IoT technologies in vehicles. As a result, the volume of data generated by vehicular sensors has increased, leading to a surge in studies focusing on driver behavior to enhance road safety and optimize transportation networks. However, traditional approaches to analyzing driver behavior have relied on supervised offline learning models, which are unsuitable for handling data streams in online learning environments. This study introduces an unsupervised online k-fix AutoCloud algorithm for detecting driver behavior patterns, leveraging the concepts of typicity and eccentricity while considering the historical-temporal relationships between samples. Furthermore, the algorithm autonomously and adaptively evolves without requiring a supervised training phase, making it compatible with the TinyML concept, encompassing Artificial Intelligence algorithms designed for low-power IoT devices. To validate the proposed method, a real case study was conducted over four days using a vehicle to compare the quantity and quality of clusters generated by the algorithm. The findings demonstrate the potential of the proposed approach for optimizing data processing with minimal computational power.","PeriodicalId":115847,"journal":{"name":"2023 IEEE International Workshop on Metrology for Automotive (MetroAutomotive)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114436745","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}

引用次数: 0

Galileo High Accuracy Service: an automotive test 伽利略高精度服务:汽车测试

2023 IEEE International Workshop on Metrology for Automotive (MetroAutomotive) Pub Date : 2023-06-28 DOI: 10.1109/MetroAutomotive57488.2023.10219123

L. Cucchi, C. Gioia, Tommaso Senni, M. Paonni

引用次数: 0

Experimental testing of a heavy-duty Diesel engine at the dynamic test bench to assess the potential of regenerative braking 在动力试验台对某重型柴油机进行了再生制动潜力的试验研究

2023 IEEE International Workshop on Metrology for Automotive (MetroAutomotive) Pub Date : 2023-06-28 DOI: 10.1109/MetroAutomotive57488.2023.10219129

Andrea Altomonte, E. Frasci, G. D. Ilio, I. Arsie, E. Jannelli

{"title":"Experimental testing of a heavy-duty Diesel engine at the dynamic test bench to assess the potential of regenerative braking","authors":"Andrea Altomonte, E. Frasci, G. D. Ilio, I. Arsie, E. Jannelli","doi":"10.1109/MetroAutomotive57488.2023.10219129","DOIUrl":"https://doi.org/10.1109/MetroAutomotive57488.2023.10219129","url":null,"abstract":"The kinetic energy recovery during braking is an essential feature of hybrid-electric vehicles, which significantly contributes to improve their fuel economy. In this work, an exhaustive performance analysis is conducted on the heavy-duty Diesel engine of a yard tractor used in port logistics, in order to estimate the potential of regenerative braking, which could be exploited in an electrified vehicle configuration.To this aim, experimental measurements are carried out at the dynamic test bench with reference to peculiar duty cycles, retrieved from an on-field measurement campaign. The on-field data are collected by means of a custom instrumentation, designed to gather real-time data from the CAN bus system of a yard tractor, equipped with the same engine, operating in the port of Salerno, Italy.First, a series of tests are carried out at the engine test bench in different steady-state and transient operating conditions, to experimentally characterize the engine performance in terms of torque, power output, and specific fuel consumption vs. engine speed and load request. Based on the engine characterization, the evaluation of engine efficiency and fuel consumption during the real duty cycles acquired on-field is performed. Furthermore, a comparison between the torque estimated by the engine control unit and the corresponding measurement by the test bench torsiometer is presented. Finally, the estimation of the kinetic energy recovery achievable by regenerative braking along the duty cycles under study is carried out, by analyzing the measured power and torque profiles. The results show that the energy recovery can significantly reduce the propulsion energy requested for performing the duty cycle.","PeriodicalId":115847,"journal":{"name":"2023 IEEE International Workshop on Metrology for Automotive (MetroAutomotive)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128697402","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}

引用次数: 0

An Enhanced Light Gradient Boosting Regressor for Virtual Sensing of CO, HC and NOx 用于CO、HC和NOx虚拟传感的增强型光梯度增强回归器

2023 IEEE International Workshop on Metrology for Automotive (MetroAutomotive) Pub Date : 2023-06-28 DOI: 10.1109/MetroAutomotive57488.2023.10219122

Emanuele Giovannardi, A. Brusa, Boris Petrone, N. Cavina, E. Corti, Massimo Barichello

{"title":"An Enhanced Light Gradient Boosting Regressor for Virtual Sensing of CO, HC and NOx","authors":"Emanuele Giovannardi, A. Brusa, Boris Petrone, N. Cavina, E. Corti, Massimo Barichello","doi":"10.1109/MetroAutomotive57488.2023.10219122","DOIUrl":"https://doi.org/10.1109/MetroAutomotive57488.2023.10219122","url":null,"abstract":"The present study introduces a novel methodology that utilizes Light Gradient Boosting Regressors to predict engine-out emissions of NOx, HC, and CO. The accuracy of the proposed models is evaluated on different types of homologation cycles. The dataset used in this study is derived from a set of 47 experimental driving cycles, including RDE, WLTC, NEDC, ECE, US06, and HWFET. The experimental driving cycles are performed on a roll bench using a spark-ignited, naturally aspirated, V12 engine-equipped vehicle. A three-second sliding window is incorporated in the models to capture the dynamic behavior of pollutant emissions. The performance of the LightGBR models is assessed using the mean absolute percentage error (MAPE) on the total pollutant mass, which is found to be 5.2% for CO, 5.7% for HC, and 6.8% for NOx. The results demonstrate the efficacy of the proposed methodology, which can be used to estimate the impact of powertrain calibration changes on pollutant emissions in a virtual environment, thereby reducing the number and the cost of the experimental tests.","PeriodicalId":115847,"journal":{"name":"2023 IEEE International Workshop on Metrology for Automotive (MetroAutomotive)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124465292","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}

引用次数: 0

Characterization of green materials for automotive acoustic comfort 绿色材料对汽车声舒适性的影响

2023 IEEE International Workshop on Metrology for Automotive (MetroAutomotive) Pub Date : 2023-06-28 DOI: 10.1109/MetroAutomotive57488.2023.10219100

C. Guarnaccia, Alessandro Ruggiero, D. Russo, M. Ferro, S. D. Iacono, P. Valášek

引用次数: 0

A Loosely Coupled Architecture for INS/GNSS Integration with Tracking Loops Aiding 一种带有跟踪环路辅助的INS/GNSS集成松耦合结构

2023 IEEE International Workshop on Metrology for Automotive (MetroAutomotive) Pub Date : 2023-06-28 DOI: 10.1109/MetroAutomotive57488.2023.10219128

F. Pisoni, N. Palella, Domenico Di Grazia, Leonardo Colombo, Giovanni Gogliettino

{"title":"A Loosely Coupled Architecture for INS/GNSS Integration with Tracking Loops Aiding","authors":"F. Pisoni, N. Palella, Domenico Di Grazia, Leonardo Colombo, Giovanni Gogliettino","doi":"10.1109/MetroAutomotive57488.2023.10219128","DOIUrl":"https://doi.org/10.1109/MetroAutomotive57488.2023.10219128","url":null,"abstract":"Loosely Coupled architectures based on Micro Electromechanical Systems (MEMS) Inertial Navigation Systems (INS), and multi-band GNSS are widely adopted to improve the availability of the integrated solution under difficult conditions, characterized by frequent obscurations of the GNSS signal. Typical scenarios include automotive urban tracks or open sky under dynamics, in presence of bridges and short tunnels. Low Earth Orbit (LEO) satellites also fall in this scope, as their lower altitude results in a Doppler curve with stronger rate variations, which sum up to receiver dynamics and are more challenging to track than traditional GNSS. In this paper, a hybrid solution is presented, where a classic loosely coupled scheme is complemented by deep integration. The INS-aided solution combines with GNSS satellites velocities and accelerations to derive code, Doppler, and Doppler rate observables predictions. Validation and performance assessment are conducted on a compact two-chip hardware platform, based on the state-of-the-art multi-band GNSS receiver STA8135 (TeseoV) and the MEMS IMU ASM330LHH from STMicroelectronics. Deep aiding has the potential to broaden the applicability of loosely coupled INS integration to the cases where signal weakness and the combined user to satellites dynamics exceed the tracking loops capability. Partially obscured automotive scenarios, reception of attenuated signals under high dynamics and assisted LEO satellites acquisition and tracking are among the applications that can benefit from the proposed scheme.","PeriodicalId":115847,"journal":{"name":"2023 IEEE International Workshop on Metrology for Automotive (MetroAutomotive)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129210208","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}

引用次数: 0

A low power NFC data over power acquisition system for high speed Electric Motor Rotors 一种用于高速电机转子的低功耗近距离通信数据采集系统

2023 IEEE International Workshop on Metrology for Automotive (MetroAutomotive) Pub Date : 2023-06-28 DOI: 10.1109/MetroAutomotive57488.2023.10219136

Mariano Nerone, Igor Valic, Matteo Zauli, N. Matteazzi, L. Marchi

{"title":"A low power NFC data over power acquisition system for high speed Electric Motor Rotors","authors":"Mariano Nerone, Igor Valic, Matteo Zauli, N. Matteazzi, L. Marchi","doi":"10.1109/MetroAutomotive57488.2023.10219136","DOIUrl":"https://doi.org/10.1109/MetroAutomotive57488.2023.10219136","url":null,"abstract":"Temperature monitoring is a key aspect in the field of electric motors. In high performance applications the rotor of a motor can reach very high temperatures leading to an over-heating of the rotor magnets, which can consequently lead to a demagnetization or to stator windings deterioration. These kinds of issues can severely impact the lifetime of an electric motor, and it is for such matter that is really important to monitor rotor temperatures: this way the motor can always be operated in a safe state, minimizing unexpected faults. Since minor errors in the simulation phase can lead to severe faults it is important to trust the simulations implemented, and this can be done by monitoring real data on the field. Such data can also be used to improve the simulation tools used to validate the motor design phase.The measurement system proposed in this paper is meant to be installed inside the motor rotor and can measure up to 8 thermocouples (type K, J, T, N, S, E, B and R) with a sampling frequency of 8 samples per second and an accuracy of ±2°C for temperatures between 0°C and 250°C. The board placed inside the rotor is powered by means of NFC energy harvest. The NFC protocol is also used to transfer data to a board placed on the motor stator.","PeriodicalId":115847,"journal":{"name":"2023 IEEE International Workshop on Metrology for Automotive (MetroAutomotive)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116253837","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}

引用次数: 0

Machine learning and impedance spectroscopy for battery state of charge evaluation 用于电池充电状态评估的机器学习和阻抗谱

2023 IEEE International Workshop on Metrology for Automotive (MetroAutomotive) Pub Date : 2023-06-28 DOI: 10.1109/MetroAutomotive57488.2023.10219121

Mattia Stighezza, R. Ferrero, V. Bianchi, I. D. Munari

{"title":"Machine learning and impedance spectroscopy for battery state of charge evaluation","authors":"Mattia Stighezza, R. Ferrero, V. Bianchi, I. D. Munari","doi":"10.1109/MetroAutomotive57488.2023.10219121","DOIUrl":"https://doi.org/10.1109/MetroAutomotive57488.2023.10219121","url":null,"abstract":"The Lithium-ion batteries market is rapidly growing. Estimating the batteries State of Charge (SOC) and their State of Health (SOH) is a challenging but crucial task, which Artificial Intelligence (AI) techniques can manage when trained with appropriate data. Physical measurements such as current, voltage and temperature during battery discharge are conventionally used as inputs of AI algorithms to provide an estimation of SOC. In this work, the effect of the battery impedance measurement on the training of a Support Vector Machine (SVM) has been studied. Electrochemical Impedance Spectroscopy (EIS) has been employed for in-situ impedance measurements at different frequencies to consider the effects of each perturbation. The obtained complex impedance values along with the measured current, voltage and temperature data, have been evaluated as features of a training set for an SVM in its regression form (SVR). To allow for simultaneous data acquisition, a module composed of 16 battery cells connected in series has undergone a total of 15 discharge cycles. Several SVR models have been trained with a variety of feature combinations, to evaluate the effect of different impedance information on the resulting model. When using the same battery cell for training and testing, the addition of magnitude and phase of the 100 Hz impedance to the input vector decreased the Root Mean Square Error (RMSE) of the estimated SOC from 1.34% to 1.09%. On the other hand, the same SVR model showed an RMSE of 1.23% when using different (but nominally identical) cells for testing.","PeriodicalId":115847,"journal":{"name":"2023 IEEE International Workshop on Metrology for Automotive (MetroAutomotive)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125331970","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}

引用次数: 1

Deep Learning for the Estimation of the Longitudinal Slip Ratio 纵向滑移比估计的深度学习

2023 IEEE International Workshop on Metrology for Automotive (MetroAutomotive) Pub Date : 2023-06-28 DOI: 10.1109/MetroAutomotive57488.2023.10219139

Raffaele Marotta, Valentin Ivanov, S. Strano, M. Terzo, C. Tordela

引用次数: 0

Anti-Interference Algorithm of Environment-Aware Millimeter Wave Radar 环境感知毫米波雷达抗干扰算法

2023 IEEE International Workshop on Metrology for Automotive (MetroAutomotive) Pub Date : 2023-06-28 DOI: 10.1109/MetroAutomotive57488.2023.10219111

Jin Dai, Shuo Sha, Yao Yao

引用次数: 0