Vishesh Kashyap, Priyanshu Mittal, B. B. Arora, A. Arora, Sourajit Bhattacharjee
{"title":"Computationally Analyzing the Impact of Spherical Depressions on the Sides of Hatchback Cars","authors":"Vishesh Kashyap, Priyanshu Mittal, B. B. Arora, A. Arora, Sourajit Bhattacharjee","doi":"10.4271/02-14-01-0008","DOIUrl":"https://doi.org/10.4271/02-14-01-0008","url":null,"abstract":"","PeriodicalId":45281,"journal":{"name":"SAE International Journal of Commercial Vehicles","volume":"14 1","pages":"111-123"},"PeriodicalIF":0.5,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47050605","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 SAE International Journal of Electrified Vehicles would like to thank and acknowledge the reviewers who have done peer reviews on articles over the last 2 years
SAE国际电动汽车杂志在此感谢并感谢在过去两年中对文章进行同行评审的审稿人
{"title":"2019-2020 Reviewers","authors":"Simona Onori","doi":"10.4271/02-13-03-0019","DOIUrl":"https://doi.org/10.4271/02-13-03-0019","url":null,"abstract":"The SAE International Journal of Electrified Vehicles would like to thank and acknowledge the reviewers who have done peer reviews on articles over the last 2 years","PeriodicalId":45281,"journal":{"name":"SAE International Journal of Commercial Vehicles","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42199772","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}
Alexander Schoen, A. Byerly, E. C. Santos, Z. Ben-Miled
This article investigates the ability of data-driven models to estimate instantaneous fuel consumption over 1 km road segments from different routes for different heavy-duty vehicles from the same fleet. Models are created using three different techniques: parametric, linear regression, and artificial neural networks. The proposed models use features derived from vehicle speed, mass, and road grade, which can be easily obtained from telematics devices, in addition to power take-off (PTO) active time, which is needed to capture the power requested by accessories in several heavy-duty vehicles. The robustness of these models with respect to the training data selection is improved by using k-fold cross-validation. Moreover, the inherent underestimation or overestimation bias of the model is calculated and used to offset the fuel consumption estimates for new routes. The study shows that the target application dictates the choice of model features. In fact, the results indicate that depending on the vocation the linear regression and neural network models, which use the same input features, are able to adequately differentiate between the fuel consumption of two
{"title":"Route-Sensitive Fuel Consumption Models for Heavy-Duty Vehicles","authors":"Alexander Schoen, A. Byerly, E. C. Santos, Z. Ben-Miled","doi":"10.4271/02-14-01-0006","DOIUrl":"https://doi.org/10.4271/02-14-01-0006","url":null,"abstract":"This article investigates the ability of data-driven models to estimate instantaneous fuel consumption over 1 km road segments from different routes for different heavy-duty vehicles from the same fleet. Models are created using three different techniques: parametric, linear regression, and artificial neural networks. The proposed models use features derived from vehicle speed, mass, and road grade, which can be easily obtained from telematics devices, in addition to power take-off (PTO) active time, which is needed to capture the power requested by accessories in several heavy-duty vehicles. The robustness of these models with respect to the training data selection is improved by using k-fold cross-validation. Moreover, the inherent underestimation or overestimation bias of the model is calculated and used to offset the fuel consumption estimates for new routes. The study shows that the target application dictates the choice of model features. In fact, the results indicate that depending on the vocation the linear regression and neural network models, which use the same input features, are able to adequately differentiate between the fuel consumption of two","PeriodicalId":45281,"journal":{"name":"SAE International Journal of Commercial Vehicles","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48530285","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}
{"title":"A New Approach of Antiskid Braking System (ABS) via Disk Pad Position Control (PPC) Method","authors":"H. Ismail, W. Chieng, S. Jeng","doi":"10.4271/02-14-01-0004","DOIUrl":"https://doi.org/10.4271/02-14-01-0004","url":null,"abstract":"","PeriodicalId":45281,"journal":{"name":"SAE International Journal of Commercial Vehicles","volume":"14 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44086982","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}
{"title":"Sensitivity Analysis of Heavy Vehicle Air Brake System to Air Leakage","authors":"S. Bagherpour, M. Akbarzadeh, S. Mouloodi","doi":"10.4271/02-14-01-0005","DOIUrl":"https://doi.org/10.4271/02-14-01-0005","url":null,"abstract":"","PeriodicalId":45281,"journal":{"name":"SAE International Journal of Commercial Vehicles","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2020-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45330966","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}
{"title":"Comparative Analysis of Emergency Evasive Steering for Long Combination Vehicles","authors":"Yang Chen, Zichen Zhang, M. Ahmadian","doi":"10.4271/02-13-03-0018","DOIUrl":"https://doi.org/10.4271/02-13-03-0018","url":null,"abstract":"","PeriodicalId":45281,"journal":{"name":"SAE International Journal of Commercial Vehicles","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2020-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44284499","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}
Li Wenli, Yu Lu, D. Guo, Wei-Dong Zheng, Haiyan Yan, Rui Xu
{"title":"Research on Road Load Simulation Technology of Commercial Vehicle Driveline Based on Chassis Dynamometer","authors":"Li Wenli, Yu Lu, D. Guo, Wei-Dong Zheng, Haiyan Yan, Rui Xu","doi":"10.4271/02-14-01-0003","DOIUrl":"https://doi.org/10.4271/02-14-01-0003","url":null,"abstract":"","PeriodicalId":45281,"journal":{"name":"SAE International Journal of Commercial Vehicles","volume":"14 1","pages":"39-47"},"PeriodicalIF":0.5,"publicationDate":"2020-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46030147","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}
{"title":"Model-Based Precise Air-Fuel Ratio Control for Gaseous Fueled Engines","authors":"Yi Han, P. Young","doi":"10.4271/02-13-03-0017","DOIUrl":"https://doi.org/10.4271/02-13-03-0017","url":null,"abstract":"","PeriodicalId":45281,"journal":{"name":"SAE International Journal of Commercial Vehicles","volume":"13 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2020-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43122439","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. Pandy, N. Pathuri, P. Salunke, Srujana Sree Subba, Daniel E. Williams
{"title":"A Practical Fail-Operational Steering Concept","authors":"A. Pandy, N. Pathuri, P. Salunke, Srujana Sree Subba, Daniel E. Williams","doi":"10.4271/02-13-03-0013","DOIUrl":"https://doi.org/10.4271/02-13-03-0013","url":null,"abstract":"","PeriodicalId":45281,"journal":{"name":"SAE International Journal of Commercial Vehicles","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2020-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47301737","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}
Vijay Sankar Anil, Tongkai Zhao, Mingjie Zhao, M. Villani, Q. Ahmed, G. Rizzoni
The ongoing electrification and data-intelligence trends in logistics industries enable efficient powertrain design and operation. In this work, the commercial package delivery vehicle powertrain design space is revisited with a specific combination of optimization and control techniques that promise accurate results with relatively fast computational time. The specific application that is explored here is a Class 6 pickup and delivery truck. A statistical learning approach is used to refine the search for the most optimal designs. Five hybrid powertrain architectures, namely, two-speed e-axle, three-speed and four-speed automatic transmission (AT) with electric motor (EM), direct-drive, and dual-motor options are explored, and a set of Pareto-optimal designs are found for a specific driving mission that represents the variations in a hypothetical operational scenario. The modeling and optimization processes are performed on the MATLAB™-Simulink platform. A cross-architecture performance and cost comparison is performed, which shows that two-speed e-axle is the optimal architecture for the selected application.
{"title":"Powertrain Design Optimization for a Range-Extended Electric Pickup and Delivery Truck","authors":"Vijay Sankar Anil, Tongkai Zhao, Mingjie Zhao, M. Villani, Q. Ahmed, G. Rizzoni","doi":"10.4271/02-13-03-0014","DOIUrl":"https://doi.org/10.4271/02-13-03-0014","url":null,"abstract":"The ongoing electrification and data-intelligence trends in logistics industries enable efficient powertrain design and operation. In this work, the commercial package delivery vehicle powertrain design space is revisited with a specific combination of optimization and control techniques that promise accurate results with relatively fast computational time. The specific application that is explored here is a Class 6 pickup and delivery truck. A statistical learning approach is used to refine the search for the most optimal designs. Five hybrid powertrain architectures, namely, two-speed e-axle, three-speed and four-speed automatic transmission (AT) with electric motor (EM), direct-drive, and dual-motor options are explored, and a set of Pareto-optimal designs are found for a specific driving mission that represents the variations in a hypothetical operational scenario. The modeling and optimization processes are performed on the MATLAB™-Simulink platform. A cross-architecture performance and cost comparison is performed, which shows that two-speed e-axle is the optimal architecture for the selected application.","PeriodicalId":45281,"journal":{"name":"SAE International Journal of Commercial Vehicles","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2020-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42825933","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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