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Investigation & Development of Vehic...
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Velazquez Alcantar, Jose.
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Investigation & Development of Vehicle Dynamics Control Frameworks for an Electric All-Wheel-Drive Hybrid Electric Vehicle.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Investigation & Development of Vehicle Dynamics Control Frameworks for an Electric All-Wheel-Drive Hybrid Electric Vehicle./
作者:
Velazquez Alcantar, Jose.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:
169 p.
附註:
Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
Contained By:
Dissertation Abstracts International79-01B(E).
標題:
Mechanical engineering. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10286017
ISBN:
9780355151923
Investigation & Development of Vehicle Dynamics Control Frameworks for an Electric All-Wheel-Drive Hybrid Electric Vehicle.
Velazquez Alcantar, Jose.
Investigation & Development of Vehicle Dynamics Control Frameworks for an Electric All-Wheel-Drive Hybrid Electric Vehicle.
- Ann Arbor : ProQuest Dissertations & Theses, 2017 - 169 p.
Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
Thesis (Ph.D.)--University of California, Davis, 2017.
This research presents an investigation and development of three unique vehicle dynamics control frameworks for an eAWD Hybrid Electric Vehicle (HEV). The eAWD HEV presents a unique engineering problem due to the fact that the front and rear axles are completely mechanically decoupled. Thus, a sophisticated control system is required to allocate torque to the front and rear axles. The control frameworks developed in the research take advantage of the multi-layered integrated vehicle dynamics control (IVDC) control structure and tailors the problem for powertrain-based vehicle dynamics control. The first control framework utilizes a simple, yet effective, optimization strategy to allocate longitudinal tire forces to the front and rear axles. A clever tire penalization strategy allows the system to allocate control to the front and rear axles while preventing tire force saturation. The second control framework utilizes the same optimization strategy to optimize and allocate slip ratio to the front and rear axles. The tire force and slip ratio allocation control frameworks are designed with implementation in mind; thus, two estimation strategies are developed to obtain accurate and robust estimates of longitudinal tire force and vehicle velocity states. The third and final control framework is designed to be the best-case framework and serves as a benchmark for the two allocation frameworks. The benchmark framework is designed utilizing Model Predictive Control (MPC) due to the fact that a complex constrained optimization is solved at every time step. The results of each control framework show that the proposed control systems are able to improve the traction control capabilities of the system and improve the handling performance of the vehicle. It is shown that the two relatively simple allocation control frameworks can achieve nearly identical performance as the benchmark MPC framework. The resulting control systems offers a unified approach to longitudinal and yaw control of the vehicle.
ISBN: 9780355151923Subjects--Topical Terms:
649730
Mechanical engineering.
Investigation & Development of Vehicle Dynamics Control Frameworks for an Electric All-Wheel-Drive Hybrid Electric Vehicle.
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This research presents an investigation and development of three unique vehicle dynamics control frameworks for an eAWD Hybrid Electric Vehicle (HEV). The eAWD HEV presents a unique engineering problem due to the fact that the front and rear axles are completely mechanically decoupled. Thus, a sophisticated control system is required to allocate torque to the front and rear axles. The control frameworks developed in the research take advantage of the multi-layered integrated vehicle dynamics control (IVDC) control structure and tailors the problem for powertrain-based vehicle dynamics control. The first control framework utilizes a simple, yet effective, optimization strategy to allocate longitudinal tire forces to the front and rear axles. A clever tire penalization strategy allows the system to allocate control to the front and rear axles while preventing tire force saturation. The second control framework utilizes the same optimization strategy to optimize and allocate slip ratio to the front and rear axles. The tire force and slip ratio allocation control frameworks are designed with implementation in mind; thus, two estimation strategies are developed to obtain accurate and robust estimates of longitudinal tire force and vehicle velocity states. The third and final control framework is designed to be the best-case framework and serves as a benchmark for the two allocation frameworks. The benchmark framework is designed utilizing Model Predictive Control (MPC) due to the fact that a complex constrained optimization is solved at every time step. The results of each control framework show that the proposed control systems are able to improve the traction control capabilities of the system and improve the handling performance of the vehicle. It is shown that the two relatively simple allocation control frameworks can achieve nearly identical performance as the benchmark MPC framework. The resulting control systems offers a unified approach to longitudinal and yaw control of the vehicle.
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