Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/84306
Title: Driving-style-based codesign optimization of an automated electric vehicle : a cyber-physical system approach
Authors: Lv, Chen
Hu, Xiaosong
Sangiovanni-Vincentelli, Alberto
Li, Yutong
Martinez, Clara Marina
Cao, Dongpu
Keywords: Automated Electric Vehicle
Codesign Optimization
DRNTU::Engineering::Mechanical engineering
Issue Date: 2018
Source: Lv, C., Hu, X., Sangiovanni-Vincentelli, A., Martinez, C. M., Li, Y., & Cao, D. (2019). Driving-style-based co-design optimization of an automated electric vehicle : a cyber-physical system approach. IEEE Transactions on Industrial Electronics, 66(4), 2965-2975. doi:10.1109/TIE.2018.2850031
Series/Report no.: IEEE Transactions on Industrial Electronics
Abstract: This paper studies the codesign optimization approach to determine how to optimally adapt automatic control of an intelligent electric vehicle to driving styles. A cyber-physical system (CPS)-based framework is proposed for codesign optimization of the plant and controller parameters for an automated electric vehicle, in view of vehicle's dynamic performance, drivability, and energy along with different driving styles. System description, requirements, constraints, optimization objectives, and methodology are investigated. Driving style recognition algorithm is developed using unsupervised machine learning and validated via vehicle experiments. Adaptive control algorithms are designed for three driving styles with different protocol selections. Performance exploration method is presented. Parameter optimizations are implemented based on the defined objective functions. Test results show that an automated vehicle with optimized plant and controller can perform its tasks well under aggressive, moderate, and conservative driving styles, further improving the overall performance. The results validate the feasibility and effectiveness of the proposed CPS-based codesign optimization approach.
URI: https://hdl.handle.net/10356/84306
http://hdl.handle.net/10220/47524
ISSN: 0278-0046
DOI: 10.1109/TIE.2018.2850031
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TIE.2018.2850031
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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