Mayuresh Bhosale, Longxiang Guo, G. Comert, Yunyi Jia
Road hazards are one of the significant sources of fatalities in road accidents. The accurate estimation of road hazards can ensure safety and enhance the driving experience. Existing methods of road condition monitoring are time-consuming, expensive, inefficient, require much human effort, and need to be regularly updated. There is a need for a flexible, cost-effective, and efficient process to detect road conditions, especially road hazards. This work presents a new method to deal with road hazards using smartphones. Since most of the population drives cars with smartphones on board, we aim to leverage this to detect road hazards more flexibly, cost-effectively, and efficiently. This paper proposes a cloud-based deep-learning road hazard detection model based on a long short-term memory (LSTM) network to detect different types of road hazards from the motion data. To address the issue of large data requests for deep learning, this paper proposes to leverage both simulation data and experimental data for the learning process. To address the issue of misdetections from an individual smartphone, we propose a cloud-based fusion approach to further improve detection accuracy. The proposed approaches are validated by experimental tests, and the results demonstrate the effectiveness of road hazard detection.
{"title":"On-Board Smartphone-Based Road Hazard Detection with Cloud-Based Fusion","authors":"Mayuresh Bhosale, Longxiang Guo, G. Comert, Yunyi Jia","doi":"10.3390/vehicles5020031","DOIUrl":"https://doi.org/10.3390/vehicles5020031","url":null,"abstract":"Road hazards are one of the significant sources of fatalities in road accidents. The accurate estimation of road hazards can ensure safety and enhance the driving experience. Existing methods of road condition monitoring are time-consuming, expensive, inefficient, require much human effort, and need to be regularly updated. There is a need for a flexible, cost-effective, and efficient process to detect road conditions, especially road hazards. This work presents a new method to deal with road hazards using smartphones. Since most of the population drives cars with smartphones on board, we aim to leverage this to detect road hazards more flexibly, cost-effectively, and efficiently. This paper proposes a cloud-based deep-learning road hazard detection model based on a long short-term memory (LSTM) network to detect different types of road hazards from the motion data. To address the issue of large data requests for deep learning, this paper proposes to leverage both simulation data and experimental data for the learning process. To address the issue of misdetections from an individual smartphone, we propose a cloud-based fusion approach to further improve detection accuracy. The proposed approaches are validated by experimental tests, and the results demonstrate the effectiveness of road hazard detection.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88268833","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}
N. Tudoroiu, M. Zaheeruddin, Roxana-Elena Tudoroiu, M. Radu, Hana Chammas
The main objective of this research paper was to develop two intelligent state estimators using shallow neural network (SNN) and NARX architectures from a large class of deep learning models. This research developed a new modelling design approach, namely, an improved hybrid adaptive neural fuzzy inference system (ANFIS) battery model, which is simple, accurate, practical, and well suited for real-time implementations in HEV/EV applications, with this being one of the main contributions of this research. On the basis of this model, we built four state of charge (SOC) estimators of high accuracy, assessed by a percentage error of less than 0.5% in a steady state compared to the 2% reported in the literature in the field. Moreover, these estimators excelled by their robustness to changes in the model parameters values and the initial “guess value” of SOC from 80–90% to 30–40%, performing in the harsh and aggressive realistic conditions of the real world, simulated by three famous driving cycle procedure tests, namely, two European standards, WLTP and NEDC, and an EPA American standard, FTP-75. Furthermore, a mean square error (MSE) of 7.97 × 10−11 for the SOC estimation of the NARX SNN SOC estimator and 5.43 × 10−6 for voltage prediction outperformed the traditional SOC estimators. Their effectiveness was proven by the performance comparison with a traditional extended Kalman filter (EKF) and adaptive nonlinear observer (ANOE) state estimators through extensive MATLAB simulations that reveal a slight superiority of the supervised learning algorithms by accuracy, online real-time implementation capability, in order to solve an extensive palette of HEV/EV applications.
{"title":"Intelligent Deep Learning Estimators of a Lithium-Ion Battery State of Charge Design and MATLAB Implementation—A Case Study","authors":"N. Tudoroiu, M. Zaheeruddin, Roxana-Elena Tudoroiu, M. Radu, Hana Chammas","doi":"10.3390/vehicles5020030","DOIUrl":"https://doi.org/10.3390/vehicles5020030","url":null,"abstract":"The main objective of this research paper was to develop two intelligent state estimators using shallow neural network (SNN) and NARX architectures from a large class of deep learning models. This research developed a new modelling design approach, namely, an improved hybrid adaptive neural fuzzy inference system (ANFIS) battery model, which is simple, accurate, practical, and well suited for real-time implementations in HEV/EV applications, with this being one of the main contributions of this research. On the basis of this model, we built four state of charge (SOC) estimators of high accuracy, assessed by a percentage error of less than 0.5% in a steady state compared to the 2% reported in the literature in the field. Moreover, these estimators excelled by their robustness to changes in the model parameters values and the initial “guess value” of SOC from 80–90% to 30–40%, performing in the harsh and aggressive realistic conditions of the real world, simulated by three famous driving cycle procedure tests, namely, two European standards, WLTP and NEDC, and an EPA American standard, FTP-75. Furthermore, a mean square error (MSE) of 7.97 × 10−11 for the SOC estimation of the NARX SNN SOC estimator and 5.43 × 10−6 for voltage prediction outperformed the traditional SOC estimators. Their effectiveness was proven by the performance comparison with a traditional extended Kalman filter (EKF) and adaptive nonlinear observer (ANOE) state estimators through extensive MATLAB simulations that reveal a slight superiority of the supervised learning algorithms by accuracy, online real-time implementation capability, in order to solve an extensive palette of HEV/EV applications.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87308810","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}
I. Kobayashi, J. Kuroda, Daigo Uchino, K. Ogawa, K. Ikeda, T. Kato, A. Endo, M. H. Peeie, T. Narita, H. Kato
The yaw acceleration required for circuit driving is determined by the time variation of the yaw rate due to two factors: corner radius and velocity at the center of gravity. Torque vectoring systems have the advantage where the yaw moment can be changed only by the longitudinal force without changing the lateral force of the tires, which greatly affects lateral acceleration. This is expected to improve the both the spinning performance and the orbital performance, which are usually in a trade-off relationship. In this study, we proposed a yaw moment control technology that actively utilized a power unit with a brake system, which was easy to implement in a system, and compared the performance of vehicles equipped with and without the proposed system using the Milliken Research Associates moment method for quasi-steady-state analysis. The performances of lateral acceleration and yaw moment were verified using the same method, and a variable corner radius simulation for circuit driving was used to compare time and performance. The results showed the effectiveness of the proposed system.
电路驱动所需的偏航加速度由转角半径和重心速度两个因素引起的偏航速率随时间的变化决定。扭矩矢量控制系统的优点是，轮胎的横向力对横向加速度的影响很大，而横向力只会改变轮胎的纵向力，从而改变轮胎的偏航力矩。这有望同时改善旋转性能和轨道性能，这通常是一种权衡关系。在本研究中，我们提出了一种偏航力矩控制技术，主动利用带有制动系统的动力单元，该技术易于在系统中实现，并使用Milliken Research Associates的力矩方法进行准稳态分析，比较了配备和不配备该系统的车辆的性能。采用相同的方法验证了横向加速度和偏航力矩的性能，并采用变转角半径仿真电路驱动来比较时间和性能。实验结果表明了该系统的有效性。
{"title":"Research on Yaw Moment Control System for Race Cars Using Drive and Brake Torques","authors":"I. Kobayashi, J. Kuroda, Daigo Uchino, K. Ogawa, K. Ikeda, T. Kato, A. Endo, M. H. Peeie, T. Narita, H. Kato","doi":"10.3390/vehicles5020029","DOIUrl":"https://doi.org/10.3390/vehicles5020029","url":null,"abstract":"The yaw acceleration required for circuit driving is determined by the time variation of the yaw rate due to two factors: corner radius and velocity at the center of gravity. Torque vectoring systems have the advantage where the yaw moment can be changed only by the longitudinal force without changing the lateral force of the tires, which greatly affects lateral acceleration. This is expected to improve the both the spinning performance and the orbital performance, which are usually in a trade-off relationship. In this study, we proposed a yaw moment control technology that actively utilized a power unit with a brake system, which was easy to implement in a system, and compared the performance of vehicles equipped with and without the proposed system using the Milliken Research Associates moment method for quasi-steady-state analysis. The performances of lateral acceleration and yaw moment were verified using the same method, and a variable corner radius simulation for circuit driving was used to compare time and performance. The results showed the effectiveness of the proposed system.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"331 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76578554","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}
The evolution toward electric vehicle nowadays appears to be the main stream in the automotive and transportation industry. In this paper, our attention is focused on the architectural modifications that should be introduced into the car body to give a proper location to the battery pack. The required battery pack is a big, heavy, and expensive component to be located, managed, climatized, maintained, and protected. This paper develops some engineering analyses and shows sketches of some possible solutions that could be adopted. The possible consequences on the position of the vehicle center of gravity, which in turn could affect the vehicle drivability, lead to locate the battery housing below the passenger compartment floor. This solution is also one of the most interesting from the point of view of the battery pack protection in case of a lateral impact and for easy serviceability and maintenance. The integration of the battery pack’s housing structure and the vehicle floor leads to a sort of sandwich structure that could have beneficial effects on the body’s stiffness (both torsional and bending). This paper also proposes some considerations that are related to the impact protection of the battery pack, with particular reference to the side impacts against a fixed obstacle, such as a pole, which are demonstrated to be the most critical. By means of some FE simulation results, the relevance of the interplay among the different parts of the vehicle side structure and battery case structure is pointed out.
{"title":"Battery Pack and Underbody: Integration in the Structure Design for Battery Electric Vehicles—Challenges and Solutions","authors":"G. Belingardi, A. Scattina","doi":"10.3390/vehicles5020028","DOIUrl":"https://doi.org/10.3390/vehicles5020028","url":null,"abstract":"The evolution toward electric vehicle nowadays appears to be the main stream in the automotive and transportation industry. In this paper, our attention is focused on the architectural modifications that should be introduced into the car body to give a proper location to the battery pack. The required battery pack is a big, heavy, and expensive component to be located, managed, climatized, maintained, and protected. This paper develops some engineering analyses and shows sketches of some possible solutions that could be adopted. The possible consequences on the position of the vehicle center of gravity, which in turn could affect the vehicle drivability, lead to locate the battery housing below the passenger compartment floor. This solution is also one of the most interesting from the point of view of the battery pack protection in case of a lateral impact and for easy serviceability and maintenance. The integration of the battery pack’s housing structure and the vehicle floor leads to a sort of sandwich structure that could have beneficial effects on the body’s stiffness (both torsional and bending). This paper also proposes some considerations that are related to the impact protection of the battery pack, with particular reference to the side impacts against a fixed obstacle, such as a pole, which are demonstrated to be the most critical. By means of some FE simulation results, the relevance of the interplay among the different parts of the vehicle side structure and battery case structure is pointed out.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90113180","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 new method to evaluate the energy consumption and driving range of electric vehicles running on intercity routes is proposed. This method consists of a hybridization of a predictive method and the application of online information during the driving run. The method uses specific algorithms for dynamic conditions based on real driving conditions, adapting the calculation method to the characteristics of the route and the driving style; electric vehicle characteristics are also taken into consideration for the driving range calculation. Real data were obtained from driving tests in a real electric vehicle under specific driving conditions and compared with the results generated by a simulation process specifically developed for the new method run under the same operating conditions as the real tests. The comparison showed very good agreement, higher than 99%. This method can be customized according to the electric vehicle characteristics, the type of route and the driving style; therefore, it shows an improvement in the determination of the real driving range for an electric vehicle since it applies real driving conditions instead of protocol statistical data.
{"title":"A Hybrid Method to Calculate the Real Driving Range of Electric Vehicles on Intercity Routes","authors":"C. Armenta-Déu, H. Cortés","doi":"10.3390/vehicles5020027","DOIUrl":"https://doi.org/10.3390/vehicles5020027","url":null,"abstract":"A new method to evaluate the energy consumption and driving range of electric vehicles running on intercity routes is proposed. This method consists of a hybridization of a predictive method and the application of online information during the driving run. The method uses specific algorithms for dynamic conditions based on real driving conditions, adapting the calculation method to the characteristics of the route and the driving style; electric vehicle characteristics are also taken into consideration for the driving range calculation. Real data were obtained from driving tests in a real electric vehicle under specific driving conditions and compared with the results generated by a simulation process specifically developed for the new method run under the same operating conditions as the real tests. The comparison showed very good agreement, higher than 99%. This method can be customized according to the electric vehicle characteristics, the type of route and the driving style; therefore, it shows an improvement in the determination of the real driving range for an electric vehicle since it applies real driving conditions instead of protocol statistical data.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83286392","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}
Umidjon Usmanov, S. Ruzimov, A. Tonoli, A. Mukhitdinov
This work represents the development of a Fuel Cell Hybrid Electric Vehicle (FCHEV) model, its validation, and the comparison of different control strategies based on the Toyota Mirai (1st generation) vehicle and its subsystems. The main investigated parameters are hydrogen consumption, and the variation of the state of charge, current, and voltage of the battery. The FCHEV model, which is made up of multiple subsystems, is developed and simulated in MATLAB® Simulink environment using a rule-based control strategy derived from the real system. The results of the model were validated using the experimental data obtained from the open-source Argonne National Laboratory (ANL) database. In the second part, the equivalent consumption minimization strategy is implemented into the controller logic to optimize the existing control strategy and investigate the difference in hydrogen consumption. It was found that the ECMS control strategy outperforms the rule-based one in all drive cycles by 0.4–15.6%. On the other hand, when compared to the real controller, ECMS performs worse for certain considered driving cycles and outperforms others.
{"title":"Modeling, Simulation and Control Strategy Optimization of Fuel Cell Hybrid Electric Vehicle","authors":"Umidjon Usmanov, S. Ruzimov, A. Tonoli, A. Mukhitdinov","doi":"10.3390/vehicles5020026","DOIUrl":"https://doi.org/10.3390/vehicles5020026","url":null,"abstract":"This work represents the development of a Fuel Cell Hybrid Electric Vehicle (FCHEV) model, its validation, and the comparison of different control strategies based on the Toyota Mirai (1st generation) vehicle and its subsystems. The main investigated parameters are hydrogen consumption, and the variation of the state of charge, current, and voltage of the battery. The FCHEV model, which is made up of multiple subsystems, is developed and simulated in MATLAB® Simulink environment using a rule-based control strategy derived from the real system. The results of the model were validated using the experimental data obtained from the open-source Argonne National Laboratory (ANL) database. In the second part, the equivalent consumption minimization strategy is implemented into the controller logic to optimize the existing control strategy and investigate the difference in hydrogen consumption. It was found that the ECMS control strategy outperforms the rule-based one in all drive cycles by 0.4–15.6%. On the other hand, when compared to the real controller, ECMS performs worse for certain considered driving cycles and outperforms others.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"115 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79108380","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}
Battery Electric Vehicles (BEVs) have an increasingly large share of the vehicle market. To ensure a safe and long operation of the mostly large underfloor-mounted traction batteries, they must be developed and tested in advance under realistic conditions. Current standards often do not provide sufficiently realistic requirements for environmental and lifetime testing, as these are mostly based on data measured on cars with an Internal Combustion Engine (ICE). Prior to this work, vibration measurements were performed on two battery-powered electric vehicles and a battery-powered commercial mini truck over various road surfaces and other influences. The measurement data are statistically evaluated so that a statement can be made about the influence of various parameters on the vibrations measured at the battery pack housing and the scatter of the influencing parameters. By creating a load profile based on the existing measurement data, current standards can be questioned and new insights gained in the development of a vibration profile for the realistic testing of battery packs for BEVs.
{"title":"Influences on Vibration Load Testing Levels for BEV Automotive Battery Packs","authors":"Till Heinzen, B. Plaumann, Marcus Kaatz","doi":"10.3390/vehicles5020025","DOIUrl":"https://doi.org/10.3390/vehicles5020025","url":null,"abstract":"Battery Electric Vehicles (BEVs) have an increasingly large share of the vehicle market. To ensure a safe and long operation of the mostly large underfloor-mounted traction batteries, they must be developed and tested in advance under realistic conditions. Current standards often do not provide sufficiently realistic requirements for environmental and lifetime testing, as these are mostly based on data measured on cars with an Internal Combustion Engine (ICE). Prior to this work, vibration measurements were performed on two battery-powered electric vehicles and a battery-powered commercial mini truck over various road surfaces and other influences. The measurement data are statistically evaluated so that a statement can be made about the influence of various parameters on the vibrations measured at the battery pack housing and the scatter of the influencing parameters. By creating a load profile based on the existing measurement data, current standards can be questioned and new insights gained in the development of a vibration profile for the realistic testing of battery packs for BEVs.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90155902","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}
Michele Pipicelli, Bernardo Sessa, Francesco de Nola, A. Gimelli, G. Di Blasio
Road transport is shifting towards electrified vehicle solutions to achieve the Conference of the Parties of the United Nations Framework Convention on Climate Change (COP27) carbon neutrality target. According to life cycle assessment analyses, battery production and disposal phases suffer a not-negligible environmental impact to be mitigated with new recycling processes, battery technology, and life-extending techniques. The foundation of this study consists of combining the assessment of vehicle efficiency and battery ageing by applying supercapacitor technology with different topologies to more conventional battery modules. The method employed here consists of analysing different hybrid energy storage system (HESS) topologies for light-duty vehicle applications over a wide range of operating conditions, including real driving cycles. A battery electric vehicle (BEV) has been modelled and validated for this aim, and the reference energy storage system was hybridised with a supercapacitor. Two HESSs with passive and semi-active topologies have been analysed and compared, and an empirical ageing model has been implemented. A rule-based control strategy has been used for the semi-active topology to manage the power split between the battery and supercapacitor. The results demonstrate that the HESS reduced the battery pack root mean square current by up to 45%, slightly improving the battery ageing. The semi-active topology performed sensibly better than the passive one, especially for small supercapacitor sizes, at the expense of more complex control strategies.
{"title":"Assessment of Battery–Supercapacitor Topologies of an Electric Vehicle under Real Driving Conditions","authors":"Michele Pipicelli, Bernardo Sessa, Francesco de Nola, A. Gimelli, G. Di Blasio","doi":"10.3390/vehicles5020024","DOIUrl":"https://doi.org/10.3390/vehicles5020024","url":null,"abstract":"Road transport is shifting towards electrified vehicle solutions to achieve the Conference of the Parties of the United Nations Framework Convention on Climate Change (COP27) carbon neutrality target. According to life cycle assessment analyses, battery production and disposal phases suffer a not-negligible environmental impact to be mitigated with new recycling processes, battery technology, and life-extending techniques. The foundation of this study consists of combining the assessment of vehicle efficiency and battery ageing by applying supercapacitor technology with different topologies to more conventional battery modules. The method employed here consists of analysing different hybrid energy storage system (HESS) topologies for light-duty vehicle applications over a wide range of operating conditions, including real driving cycles. A battery electric vehicle (BEV) has been modelled and validated for this aim, and the reference energy storage system was hybridised with a supercapacitor. Two HESSs with passive and semi-active topologies have been analysed and compared, and an empirical ageing model has been implemented. A rule-based control strategy has been used for the semi-active topology to manage the power split between the battery and supercapacitor. The results demonstrate that the HESS reduced the battery pack root mean square current by up to 45%, slightly improving the battery ageing. The semi-active topology performed sensibly better than the passive one, especially for small supercapacitor sizes, at the expense of more complex control strategies.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83601053","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}
Sascha Krysmon, Johannes Claßen, S. Pischinger, Georgi Trendafilov, Marc Düzgün, Frank Dorscheidt
The topics of climate change and pollutant emission reduction are dominating societal discussions in many areas. In automotive development, with the introduction of real driving emissions (RDE) testing and the upcoming EU7 legislation, there are endless boundary conditions and potential scenarios that need to be evaluated. In terms of vehicle calibration, this is leading to a strong focus on alternative approaches such as virtual calibration. Due to the flexibility of virtual test environments and the variety of RDE scenarios, the amount of data collected is rapidly increasing. Supporting the calibration engineers in using the available data and identifying relevant information and test scenarios requires efficient approaches to data analysis. This paper therefore discusses the potential of data clustering to support this process. Using a previously developed approach for event detection in emission calibration, a methodology for the automatic categorization of events is presented. Approaches to clustering algorithms (hierarchical, partitioning, and density-based) are discussed and applied to data of interest. Their suitability for different signals is investigated exemplarily, and the relevant inputs are analyzed for their usability in calibration procedures. It is shown which clustering approaches have the potential to be implemented in the vehicle calibration process to provide added value to data evaluation by calibration engineers.
{"title":"RDE Calibration—Evaluating Fundamentals of Clustering Approaches to Support the Calibration Process","authors":"Sascha Krysmon, Johannes Claßen, S. Pischinger, Georgi Trendafilov, Marc Düzgün, Frank Dorscheidt","doi":"10.3390/vehicles5020023","DOIUrl":"https://doi.org/10.3390/vehicles5020023","url":null,"abstract":"The topics of climate change and pollutant emission reduction are dominating societal discussions in many areas. In automotive development, with the introduction of real driving emissions (RDE) testing and the upcoming EU7 legislation, there are endless boundary conditions and potential scenarios that need to be evaluated. In terms of vehicle calibration, this is leading to a strong focus on alternative approaches such as virtual calibration. Due to the flexibility of virtual test environments and the variety of RDE scenarios, the amount of data collected is rapidly increasing. Supporting the calibration engineers in using the available data and identifying relevant information and test scenarios requires efficient approaches to data analysis. This paper therefore discusses the potential of data clustering to support this process. Using a previously developed approach for event detection in emission calibration, a methodology for the automatic categorization of events is presented. Approaches to clustering algorithms (hierarchical, partitioning, and density-based) are discussed and applied to data of interest. Their suitability for different signals is investigated exemplarily, and the relevant inputs are analyzed for their usability in calibration procedures. It is shown which clustering approaches have the potential to be implemented in the vehicle calibration process to provide added value to data evaluation by calibration engineers.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89636633","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}
The recovery of kinetic energy (KER) in electric vehicles was analyzed and characterized. Two main systems were studied: the use of regenerative brakes, and the conversion of potential energy. The paper shows that potential energy is a potential source of kinetic energy recovery with higher efficiency than the traditional system of regenerative brakes. The study compared the rate of KER in both cases for a BMWi3 electric vehicle operating under specific driving conditions; the results of the analysis showed that potential energy conversion can recover up to 88.2%, while the maximum efficiency attained with the regenerative brake system was 60.1%. The study concluded that in driving situations with sudden and frequent changes of vehicle speed due to traffic conditions, such as in urban routes, the use of regenerative brakes was shown to be the best option for KER; however, in intercity routes, driving conditions favored the use of potential energy as a priority system for KER.
{"title":"Analysis of Kinetic Energy Recovery Systems in Electric Vehicles","authors":"C. Armenta-Déu, H. Cortés","doi":"10.3390/vehicles5020022","DOIUrl":"https://doi.org/10.3390/vehicles5020022","url":null,"abstract":"The recovery of kinetic energy (KER) in electric vehicles was analyzed and characterized. Two main systems were studied: the use of regenerative brakes, and the conversion of potential energy. The paper shows that potential energy is a potential source of kinetic energy recovery with higher efficiency than the traditional system of regenerative brakes. The study compared the rate of KER in both cases for a BMWi3 electric vehicle operating under specific driving conditions; the results of the analysis showed that potential energy conversion can recover up to 88.2%, while the maximum efficiency attained with the regenerative brake system was 60.1%. The study concluded that in driving situations with sudden and frequent changes of vehicle speed due to traffic conditions, such as in urban routes, the use of regenerative brakes was shown to be the best option for KER; however, in intercity routes, driving conditions favored the use of potential energy as a priority system for KER.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89129990","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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