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You searched for subject:(Electrified Vehicles). Showing records 1 – 2 of 2 total matches.

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1. Divakarla, Kavya Prabha. A Cognitive Advanced Driver Assistance Systems (ADAS) Architecture for Autonomous-capable Electrified Vehicles.

Degree: PhD, 2019, McMaster University

The automotive industry is seen to be making a monumental paradigm shift from manual to semi-autonomous to fully Autonomous Vehicles. An Advanced Driver Assistance System (ADAS) forms a major building block for realizing these next generation of highly Autonomous-capable Vehicles. Although the general ADAS architecture is widely discussed, limited details are available about the functionality of the modules and their interactions, backed up by scientific justification. This limits the utilization of such an architecture for pragmatic implementation. A Cognitive ADAS Architecture for level 4 Autonomous-capable Electrified Vehicles (EV) is proposed in this thesis. Variations for levels 3 and 3.5 (combination of levels 3 and 4, with the primary fallback through a human driver and the secondary through an Automated Driving System) are also presented. A validated simulation framework is built for highway driving based on the proposed level 4 architecture for an enhanced Tesla Model S. It was concluded that the autonomous control provided a 28% energy economy increase, on average, compared to human driver control. Through a quantitative sensitivity analysis, the optimal Mission/Motion Planning and energy management are seen in addition to a positive impact on the EV battery, motor, and dynamics, realized from the minimized instantaneous fluctuations. These factors are considered to contribute to this significant increase in the energy economy of an autonomous-controlled EV. Furthermore, this impact was seen to be relatively higher for autonomous longitudinal vehicle control compared to lateral. This difference in the improved operation of the Autonomous-capable EV components between the Automated Driving System and the human driver control was seen to be the highest for the battery current. In overall, an increase in vehicle autonomy, resulted in an improvement in the EV performance, dynamics and operation of the battery and motor, compared to a human driver control.

Thesis

Doctor of Philosophy (PhD)

Advisors/Committee Members: Emadi, Ali, Razavi, Saiedeh, Electrical and Computer Engineering.

Subjects/Keywords: Autonomous Vehicles; Electrified Vehicles; Architecture

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Divakarla, K. P. (2019). A Cognitive Advanced Driver Assistance Systems (ADAS) Architecture for Autonomous-capable Electrified Vehicles. (Doctoral Dissertation). McMaster University. Retrieved from http://hdl.handle.net/11375/23832

Chicago Manual of Style (16th Edition):

Divakarla, Kavya Prabha. “A Cognitive Advanced Driver Assistance Systems (ADAS) Architecture for Autonomous-capable Electrified Vehicles.” 2019. Doctoral Dissertation, McMaster University. Accessed December 03, 2020. http://hdl.handle.net/11375/23832.

MLA Handbook (7th Edition):

Divakarla, Kavya Prabha. “A Cognitive Advanced Driver Assistance Systems (ADAS) Architecture for Autonomous-capable Electrified Vehicles.” 2019. Web. 03 Dec 2020.

Vancouver:

Divakarla KP. A Cognitive Advanced Driver Assistance Systems (ADAS) Architecture for Autonomous-capable Electrified Vehicles. [Internet] [Doctoral dissertation]. McMaster University; 2019. [cited 2020 Dec 03]. Available from: http://hdl.handle.net/11375/23832.

Council of Science Editors:

Divakarla KP. A Cognitive Advanced Driver Assistance Systems (ADAS) Architecture for Autonomous-capable Electrified Vehicles. [Doctoral Dissertation]. McMaster University; 2019. Available from: http://hdl.handle.net/11375/23832


University of Michigan

2. Wu, Kai. Real-Time Energy Management and Transient Power Control for Fuel Cell Electrified Vehicles.

Degree: PhD, Mechanical Engineering, 2019, University of Michigan

Automotive OEMs have responded to energy and environmental concerns with mass-produced Hybrid Electric Vehicles (HEVs), Plug-in Hybrid Electric Vehicles (PHEVs), and Battery Electric Vehicles (BEVs) that satisfy various customers’ demands. While the sales volume of these vehicles continues to climb, OEMs recognize that Fuel Cell Vehicles (FCVs) could be the ultimate solution to electrification of personal transportation. Thus, they have forged ahead with developing commercial FCV technologies. However, several challenges exist in bringing Fuel Cell technology to mass production. Aside from steep costs, energy management for achieving total optimal system efficiency in real-time and under all driving conditions is still under development. There is room for improvement in controlling the transient power balance between the Fuel Cell System (FCS), high voltage battery, and driver demand, calling for a systematic framework and new tools to understand and address the FCS dynamic effects. This dissertation is devoted to providing a comprehensive framework for analyzing the dynamic effects of FCS on optimal energy management applications, and developing a hierarchical control framework for real-time energy management. Dynamic characteristics of a Proton Exchange Membrane Fuel Cell (PEMFC) system can impact fuel economy and load following performance of an FCV, especially if these dynamics are not considered when designing the top-level energy management strategy. To quantify the effects of FCS dynamics on optimal energy management, Dynamic Programming (DP) is adopted in this dissertation to derive optimal power split strategies at two levels: Level 1, where the FCS dynamics are ignored; and Level 2, where the FCS dynamics are incorporated. Analysis is performed to quantify the differences between these two strategies to understand the effects of FCS dynamics. The results show that ignoring slow FCS dynamics in DP can lead to several problems, including deteriorated power tracking, violation of charge sustaining performance, and loss of fuel economy. For the FCVs with fast power dynamics, an optimization-oriented supervisory controller based on Pontryagin's Minimum Principle (PMP) is proposed. The Adaptive-PMP (A-PMP) method inherits the advantages of model-based optimization to formulate a Hamiltonian and convert the trajectory optimization problem into pointwise-in-time optimization problem, where the co-state value is estimated and adapted based on average power and total travel time. A-PMP is evaluated on a high fidelity FCV powertrain model. Comparing to the default baseline energy management method, A-PMP yields better performance in fuel economy. Furthermore, a preliminary vehicle test shows up to 5.9% of improvement in fuel economy over an OEM's rule-based strategy. For the FCVs with slow power dynamics, an online energy management algorithm is proposed to mitigate the dynamic effects of FCS while maintaining a near-optimal fuel economy. The A-PMP-Model Predictive Control (APMP-MPC) scheme includes a top level… Advisors/Committee Members: Sun, Jing (committee member), Kolmanovsky, Ilya Vladimir (committee member), Ersal, Tulga (committee member), Peng, Huei (committee member).

Subjects/Keywords: Real-time Energy Management; Transient Power Control; Fuel Cell Electrified Vehicles; Hybrid Vehicles; Model Predictive Control; Optimization; Mechanical Engineering; Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wu, K. (2019). Real-Time Energy Management and Transient Power Control for Fuel Cell Electrified Vehicles. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/149914

Chicago Manual of Style (16th Edition):

Wu, Kai. “Real-Time Energy Management and Transient Power Control for Fuel Cell Electrified Vehicles.” 2019. Doctoral Dissertation, University of Michigan. Accessed December 03, 2020. http://hdl.handle.net/2027.42/149914.

MLA Handbook (7th Edition):

Wu, Kai. “Real-Time Energy Management and Transient Power Control for Fuel Cell Electrified Vehicles.” 2019. Web. 03 Dec 2020.

Vancouver:

Wu K. Real-Time Energy Management and Transient Power Control for Fuel Cell Electrified Vehicles. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2020 Dec 03]. Available from: http://hdl.handle.net/2027.42/149914.

Council of Science Editors:

Wu K. Real-Time Energy Management and Transient Power Control for Fuel Cell Electrified Vehicles. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/149914

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