Pub Date : 2020-11-01DOI: 10.1109/VPPC49601.2020.9330979
R. German, J. Jaguemont, A. Bouscayrol
This paper studies the effect of the granularity of a cell model on the voltage accuracy. A multi-coupled cell model with varying parameters is compared with a simpler one (varying voltage source and equivalent series resistance). The first model implies a very long and complex characterization process. The second one is very simple. Experiments are performed at different temperatures by applying a current profile corresponding to a driving cycle of an electric vehicle. Experimental results show both models can be used for $25^{circ}mathrm{C}$ and $10^{circ}mathrm{C}$ ambient temperatures with reasonable accuracy. Nevertheless, when the temperature is cold the multi-coupled model is more accurate.
{"title":"Comparison of two levels of cell models for an EV current cycle","authors":"R. German, J. Jaguemont, A. Bouscayrol","doi":"10.1109/VPPC49601.2020.9330979","DOIUrl":"https://doi.org/10.1109/VPPC49601.2020.9330979","url":null,"abstract":"This paper studies the effect of the granularity of a cell model on the voltage accuracy. A multi-coupled cell model with varying parameters is compared with a simpler one (varying voltage source and equivalent series resistance). The first model implies a very long and complex characterization process. The second one is very simple. Experiments are performed at different temperatures by applying a current profile corresponding to a driving cycle of an electric vehicle. Experimental results show both models can be used for $25^{circ}mathrm{C}$ and $10^{circ}mathrm{C}$ ambient temperatures with reasonable accuracy. Nevertheless, when the temperature is cold the multi-coupled model is more accurate.","PeriodicalId":6851,"journal":{"name":"2020 IEEE Vehicle Power and Propulsion Conference (VPPC)","volume":"30 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78982247","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}
Pub Date : 2020-11-01DOI: 10.1109/VPPC49601.2020.9330852
S. Schlimpert, Branimir Mrak, Ilja Siera, R. Sprangers, J. Nonneman, M. Paepe, Steven Vanhee
The development of the next generation electrical vehicles requires drive-trains to become more compact, high-performant, and robust at the lowest possible cost. These more compact drive-trains operate at the same power ratings as their bigger sized equivalent and do need to dissipate their heat in a smaller volume. Therefore, more advanced liquid cooling methods of the drive-train components are needed to enhance the heat removal and increase the compactness, i.e., power density. Till now, most advanced cooled switched reluctance motors (SRM) of such drive-trains use already liquid cooling, i.e., Water& Glycol (WG) in a jacket. However, this liquid cooling method has only an indirect contact with the coils of the motor, i.e., is limited in thermal performance. Therefore, this paper studies direct coil cooling methods and specifically the direct oil jet cooling approach in terms of power density increase experimentally. In addition, the challenge of validating properly the experimental data of several innovative direct coil cooling concepts by commercial software packages will be discussed in the paper.
{"title":"Experimental & Modelling Study of Advanced Direct Coil Cooling Methods in a Switched Reluctance Motor","authors":"S. Schlimpert, Branimir Mrak, Ilja Siera, R. Sprangers, J. Nonneman, M. Paepe, Steven Vanhee","doi":"10.1109/VPPC49601.2020.9330852","DOIUrl":"https://doi.org/10.1109/VPPC49601.2020.9330852","url":null,"abstract":"The development of the next generation electrical vehicles requires drive-trains to become more compact, high-performant, and robust at the lowest possible cost. These more compact drive-trains operate at the same power ratings as their bigger sized equivalent and do need to dissipate their heat in a smaller volume. Therefore, more advanced liquid cooling methods of the drive-train components are needed to enhance the heat removal and increase the compactness, i.e., power density. Till now, most advanced cooled switched reluctance motors (SRM) of such drive-trains use already liquid cooling, i.e., Water& Glycol (WG) in a jacket. However, this liquid cooling method has only an indirect contact with the coils of the motor, i.e., is limited in thermal performance. Therefore, this paper studies direct coil cooling methods and specifically the direct oil jet cooling approach in terms of power density increase experimentally. In addition, the challenge of validating properly the experimental data of several innovative direct coil cooling concepts by commercial software packages will be discussed in the paper.","PeriodicalId":6851,"journal":{"name":"2020 IEEE Vehicle Power and Propulsion Conference (VPPC)","volume":"1 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89464896","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}
Pub Date : 2020-11-01DOI: 10.1109/VPPC49601.2020.9330931
Ahmed M. Ali, M. Yacoub
Optimal predictive power management strategies (PMSs) for hybrid electric vehicles have a significant potentiality to achieve near-optimal solutions in real-time. Providing a priori prediction for power demand and implementing simplified, yet accurate, driveline models, to yield an optimal control strategy online are key challenges for predictive power management algorithms. Finding suitable solutions to resolve these challenges contributes to the ability of real-time PMSs to define efficient power handling strategies and hence promote better energy efficiency in electrified powertrains. This paper presents a neural networks-based predictive PMSs for fuel cell vehicles. The proposed method implements two types of networks, time-delay and nonlinear autoregressive network with exogenous inputs, to generate the required predictive models for the PMS. The online control module investigates an optimal power split strategy over the predicted horizon, considering minimal energy consumption and on-board charge retention. For comparative evaluation, rule-based method and the global optimal solution for a test driving cycle are considered. Results analysis revealed the ability of proposed method to yield an improvement of 20.71 % in energy efficiency without mitigating the state-of-charge on energy storage systems.
{"title":"Optimal predictive power management strategy for fuel cell electric vehicles using neural networks in real-time","authors":"Ahmed M. Ali, M. Yacoub","doi":"10.1109/VPPC49601.2020.9330931","DOIUrl":"https://doi.org/10.1109/VPPC49601.2020.9330931","url":null,"abstract":"Optimal predictive power management strategies (PMSs) for hybrid electric vehicles have a significant potentiality to achieve near-optimal solutions in real-time. Providing a priori prediction for power demand and implementing simplified, yet accurate, driveline models, to yield an optimal control strategy online are key challenges for predictive power management algorithms. Finding suitable solutions to resolve these challenges contributes to the ability of real-time PMSs to define efficient power handling strategies and hence promote better energy efficiency in electrified powertrains. This paper presents a neural networks-based predictive PMSs for fuel cell vehicles. The proposed method implements two types of networks, time-delay and nonlinear autoregressive network with exogenous inputs, to generate the required predictive models for the PMS. The online control module investigates an optimal power split strategy over the predicted horizon, considering minimal energy consumption and on-board charge retention. For comparative evaluation, rule-based method and the global optimal solution for a test driving cycle are considered. Results analysis revealed the ability of proposed method to yield an improvement of 20.71 % in energy efficiency without mitigating the state-of-charge on energy storage systems.","PeriodicalId":6851,"journal":{"name":"2020 IEEE Vehicle Power and Propulsion Conference (VPPC)","volume":"110 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80552690","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}
Pub Date : 2020-11-01DOI: 10.1109/VPPC49601.2020.9330976
Xiao Luo, Yunqing Hu, Yue Liu, Hu Mengying, Wei Chu, Jun Lin
Integrity of rails is the foundation of safe rail transportation. It is critical to detect internal rail flaws in time, and one popular solution to this issue is ultrasonic techniques. On the other hand, long short-term memory (LSTM) has been proven in text classification to which we think the ultrasonic rail flaw detection can be quite similar. In this context, this paper proposes a novel text-style sequential modeling method for ultrasonic rail flaw data and a LSTM-based deep learning model for rail flaw detection. Comparative experiments proved the feasibility and remarkable computational efficiency of the proposed modeling method and model.
{"title":"A novel text-style sequential modeling method for ultrasonic rail flaw detection","authors":"Xiao Luo, Yunqing Hu, Yue Liu, Hu Mengying, Wei Chu, Jun Lin","doi":"10.1109/VPPC49601.2020.9330976","DOIUrl":"https://doi.org/10.1109/VPPC49601.2020.9330976","url":null,"abstract":"Integrity of rails is the foundation of safe rail transportation. It is critical to detect internal rail flaws in time, and one popular solution to this issue is ultrasonic techniques. On the other hand, long short-term memory (LSTM) has been proven in text classification to which we think the ultrasonic rail flaw detection can be quite similar. In this context, this paper proposes a novel text-style sequential modeling method for ultrasonic rail flaw data and a LSTM-based deep learning model for rail flaw detection. Comparative experiments proved the feasibility and remarkable computational efficiency of the proposed modeling method and model.","PeriodicalId":6851,"journal":{"name":"2020 IEEE Vehicle Power and Propulsion Conference (VPPC)","volume":"28 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83721800","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}
Pub Date : 2020-11-01DOI: 10.1109/VPPC49601.2020.9330842
Josep Salvador-Iborra, Jürgen Schneider, R. Tatschl
As simulation of complex systems gets closer to reality, model-based development offers the opportunity to virtualize an increasing number of powertrain design and testing tasks. Many tasks are best dealt with using a combination of 3D and 1D modeling. To achieve this, one efficient technique is the conversion of high-fidelity 3D models into more flexible 1D models. This paper presents a novel automated workflow that allows to create a 1D lumped-element network using data from a 3D thermal simulation as input. Data collection is accomplished by an embedded algorithm that emulates the steps an expert would take to derive an equivalent 1D model. Generation of the lumped-element network using the collected data is also automatic. The method is tested and validated using a model of a pouch cell used in automotive batteries. Validation studies show very good agreement between the results of the original 3D model and the equivalent 1D model. Equivalence is maintained even when the model is extrapolated from ⅕ to 4 times the parametrization thermal load. This outcome encourages further development of the tool and application to more complex systems.
{"title":"Automatic Conversion of a 3D Thermal Model of a Battery Cell into a 1D Lumped-Element Network : Paper for special session 8 - Multi-level Models for Simulation of Electrified Vehicles","authors":"Josep Salvador-Iborra, Jürgen Schneider, R. Tatschl","doi":"10.1109/VPPC49601.2020.9330842","DOIUrl":"https://doi.org/10.1109/VPPC49601.2020.9330842","url":null,"abstract":"As simulation of complex systems gets closer to reality, model-based development offers the opportunity to virtualize an increasing number of powertrain design and testing tasks. Many tasks are best dealt with using a combination of 3D and 1D modeling. To achieve this, one efficient technique is the conversion of high-fidelity 3D models into more flexible 1D models. This paper presents a novel automated workflow that allows to create a 1D lumped-element network using data from a 3D thermal simulation as input. Data collection is accomplished by an embedded algorithm that emulates the steps an expert would take to derive an equivalent 1D model. Generation of the lumped-element network using the collected data is also automatic. The method is tested and validated using a model of a pouch cell used in automotive batteries. Validation studies show very good agreement between the results of the original 3D model and the equivalent 1D model. Equivalence is maintained even when the model is extrapolated from ⅕ to 4 times the parametrization thermal load. This outcome encourages further development of the tool and application to more complex systems.","PeriodicalId":6851,"journal":{"name":"2020 IEEE Vehicle Power and Propulsion Conference (VPPC)","volume":"34 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86970685","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}
Dropper's breakage and slack damage the stability of the high-speed railway power supply system and reduce safety. Manual inspection to monitor the dropper and guide maintenance is dangerous and inefficient. Therefore, we propose an automatic dropper breakage and slack monitoring method. Dropper's candidate regions are selected through prior knowledge, and an end-to-end detection network is designed to locate and identify the fault. To overcome the imbalance between the normal and faulty samples, data augmentation and gradient harmonized loss are adopted. Experiments showed that the MAP is 86.2% and it cost 39.4ms per frame, and the method can effectively monitor high-speed railway droppers.
{"title":"End-to-end High-speed Railway Dropper Breakage and Slack Monitoring Based on Computer Vision","authors":"Shiwang Liu, Yunqing Hu, Jun Lin, Hao Yuan, Qunfang Xiong, Wei Yue","doi":"10.1109/VPPC49601.2020.9330983","DOIUrl":"https://doi.org/10.1109/VPPC49601.2020.9330983","url":null,"abstract":"Dropper's breakage and slack damage the stability of the high-speed railway power supply system and reduce safety. Manual inspection to monitor the dropper and guide maintenance is dangerous and inefficient. Therefore, we propose an automatic dropper breakage and slack monitoring method. Dropper's candidate regions are selected through prior knowledge, and an end-to-end detection network is designed to locate and identify the fault. To overcome the imbalance between the normal and faulty samples, data augmentation and gradient harmonized loss are adopted. Experiments showed that the MAP is 86.2% and it cost 39.4ms per frame, and the method can effectively monitor high-speed railway droppers.","PeriodicalId":6851,"journal":{"name":"2020 IEEE Vehicle Power and Propulsion Conference (VPPC)","volume":"7 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75428361","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}
Pub Date : 2020-11-01DOI: 10.1109/vppc49601.2020.9330823
{"title":"Welcome from the Chair of the VPPC Steering Committee","authors":"","doi":"10.1109/vppc49601.2020.9330823","DOIUrl":"https://doi.org/10.1109/vppc49601.2020.9330823","url":null,"abstract":"","PeriodicalId":6851,"journal":{"name":"2020 IEEE Vehicle Power and Propulsion Conference (VPPC)","volume":"155 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72655427","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}
Pub Date : 2020-11-01DOI: 10.1109/VPPC49601.2020.9330946
Lukas Hott, V. Ivanov, K. Augsburg, Vincenzo Ricciardi, M. Dhaens, M. A. Sakka, K. Praet, J. V. Molina
This paper presents a controller for enhancing the ride comfort of electric vehicles with in-wheel motors (IWM) and electromagnetic suspensions (AS). The combined use of IWMs and AS to increase the ride comfort is referred to as Ride Blending (RB). The purpose of this integrated control, its general idea and concept are discussed. The Ride Blending controller is based on a multi-layer hierarchical control architecture. To continuously allocate the demand between the actuators, the control makes use of a cost function optimisation where the ideal control parameters for the current time step are defined. The goal of each component of this function is explained and the structure of each one is described. The use of the ride blending control is then demonstrated on various driving manoeuvres to show the functionality and the ride quality improvement.
{"title":"Ride Blending Control for AWD Electric Vehicle with In-Wheel Motors and Electromagnetic Suspension","authors":"Lukas Hott, V. Ivanov, K. Augsburg, Vincenzo Ricciardi, M. Dhaens, M. A. Sakka, K. Praet, J. V. Molina","doi":"10.1109/VPPC49601.2020.9330946","DOIUrl":"https://doi.org/10.1109/VPPC49601.2020.9330946","url":null,"abstract":"This paper presents a controller for enhancing the ride comfort of electric vehicles with in-wheel motors (IWM) and electromagnetic suspensions (AS). The combined use of IWMs and AS to increase the ride comfort is referred to as Ride Blending (RB). The purpose of this integrated control, its general idea and concept are discussed. The Ride Blending controller is based on a multi-layer hierarchical control architecture. To continuously allocate the demand between the actuators, the control makes use of a cost function optimisation where the ideal control parameters for the current time step are defined. The goal of each component of this function is explained and the structure of each one is described. The use of the ride blending control is then demonstrated on various driving manoeuvres to show the functionality and the ride quality improvement.","PeriodicalId":6851,"journal":{"name":"2020 IEEE Vehicle Power and Propulsion Conference (VPPC)","volume":"3 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74554389","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}
Pub Date : 2020-11-01DOI: 10.1109/VPPC49601.2020.9330821
Kaibing Du, Zhanchao Wang, Zhengfang Zhang
Heavy haul train is large inertial and non-linear systems. Many real-time disturbances have a significant impact on autonomous driving control. In order to improve the effect of autonomous control, a new state observation and parameter identification method is proposed. The longitudinal multi-mass dynamics model is established for describing the train performance. The acceleration is calculated by Kalman filter of sampled speed. Resistance force and air braking response are identified by train dynamic model. The state observation method can significantly improve autonomous driving control effects. This method is used in control of heavy train autonomous driving.
{"title":"State Observation and Parameter Identification for Autonomous Heavy Haul Train","authors":"Kaibing Du, Zhanchao Wang, Zhengfang Zhang","doi":"10.1109/VPPC49601.2020.9330821","DOIUrl":"https://doi.org/10.1109/VPPC49601.2020.9330821","url":null,"abstract":"Heavy haul train is large inertial and non-linear systems. Many real-time disturbances have a significant impact on autonomous driving control. In order to improve the effect of autonomous control, a new state observation and parameter identification method is proposed. The longitudinal multi-mass dynamics model is established for describing the train performance. The acceleration is calculated by Kalman filter of sampled speed. Resistance force and air braking response are identified by train dynamic model. The state observation method can significantly improve autonomous driving control effects. This method is used in control of heavy train autonomous driving.","PeriodicalId":6851,"journal":{"name":"2020 IEEE Vehicle Power and Propulsion Conference (VPPC)","volume":"131 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77544437","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}
Pub Date : 2020-11-01DOI: 10.1109/VPPC49601.2020.9330958
Meiling Yue, S. Jemei, N. Zerhouni
PEM Fuel cells, characterized by low operating temperature, fast response, high energy density and high efficiency, have found their place in automotive applications. However, the durability of on-board fuel cells is facing challenges. Aiming at improving system durability and reliability, prognostics and health management, as a smart manufacturing discipline, have been applied to monitor the system health state and protect the system integrity. This paper proposes to combine prognostics when developing energy management strategies for fuel cell electric vehicles, which is used to assess and predict the fuel cell performance. This paper has also considered the uncertainties of the prognostics results and a prognostics-enabled decision-making process is designed as the post-prognostics process to perform energy management in a fuel cell hybrid electric vehicle.
{"title":"Prognostics-based Energy Management in Fuel Cell Hybrid Electric Vehicle Considering RUL Uncertainty","authors":"Meiling Yue, S. Jemei, N. Zerhouni","doi":"10.1109/VPPC49601.2020.9330958","DOIUrl":"https://doi.org/10.1109/VPPC49601.2020.9330958","url":null,"abstract":"PEM Fuel cells, characterized by low operating temperature, fast response, high energy density and high efficiency, have found their place in automotive applications. However, the durability of on-board fuel cells is facing challenges. Aiming at improving system durability and reliability, prognostics and health management, as a smart manufacturing discipline, have been applied to monitor the system health state and protect the system integrity. This paper proposes to combine prognostics when developing energy management strategies for fuel cell electric vehicles, which is used to assess and predict the fuel cell performance. This paper has also considered the uncertainties of the prognostics results and a prognostics-enabled decision-making process is designed as the post-prognostics process to perform energy management in a fuel cell hybrid electric vehicle.","PeriodicalId":6851,"journal":{"name":"2020 IEEE Vehicle Power and Propulsion Conference (VPPC)","volume":"32 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81544056","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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