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http://hdl.handle.net/11375/27754
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DC Field | Value | Language |
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dc.contributor.advisor | Bauman, Jennifer | - |
dc.contributor.author | Meshginqalam, Ata | - |
dc.date.accessioned | 2022-08-19T19:15:49Z | - |
dc.date.available | 2022-08-19T19:15:49Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | http://hdl.handle.net/11375/27754 | - |
dc.description.abstract | Electric vehicles with autonomous driving are the future of transportation, as they are sustainable, efficient, environmentally friendly, and can provide collision-free congestion-free driving. However, the sensing and control technology adds new accessory loads which increase the vehicle energy use. Thus, it is critical to minimize energy use where possible, and optimal speed planning is a promising way to achieve this goal and is thus the topic of study for this thesis. First, a low-computation framework for the onboard calculation of energy-optimal cruising speed of battery electric vehicles is proposed. The framework is used to investigate the critical parameters for energy-optimal cruising speed determination, and it includes major internal and external vehicle losses, uses accurate motor-inverter efficiency maps as look-up tables, and does not require knowledge of the future route. This framework is validated using three electric vehicle models in MATLAB/SIMULINK. Secondly, a novel two-level model predictive control (MPC) speed control algorithm for battery electric autonomous vehicles as a successive convex optimization problem is proposed. The proposed successive convex approach produces a highly accurate optimal speed profile while also being solvable in real-time with the vehicle's onboard computing resources. This algorithm is used to perform a variety of simulated test cases, which show an energy savings potential of about 1% to 20% for different driving conditions, compared to a non-energy-optimal driving profile. Lastly, the research is expanded to consider fuel cell hybrid electric vehicles (FCHEVs), which have the added need for an optimal energy management strategy inv addition to optimal speed planning. Novel successive and integrated convex speed planning and energy management algorithms are proposed to solve the minimum hydrogen consumption problem for autonomous FCHEVs. The simulation results show that the proposed integrated method, which considers fuel cell system efficiency in the optimization objective function for speed planning, leads to 0.19% to 2.37% less hydrogen consumption compared to the successive method on short drive cycles with varying accessory loads. On the same test cycles, the integrated method uses 10.12% to 21.62% less hydrogen than an arbitrary constant-speed profile. | en_US |
dc.language.iso | en | en_US |
dc.subject | Optimization | en_US |
dc.subject | Electric vehicles | en_US |
dc.subject | speed profile | en_US |
dc.subject | EMS | en_US |
dc.title | OPTIMAL SPEED PLANNING TO MINIMIZE ENERGY USE OF AUTONOMOUS BATTERY ELECTRIC AND FUEL CELL HYBRID ELECTRIC VEHICLES | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Electrical and Computer Engineering | en_US |
dc.description.degreetype | Thesis | en_US |
dc.description.degree | Doctor of Philosophy (PhD) | en_US |
dc.description.layabstract | Autonomous vehicles are expected to be the future of transportation, however, the high continuous electrical accessory power needed for control and perception is a challenge. Fortunately, there is an inherent property of speed planning for autonomous vehicles that can help deal with this problem. This thesis focuses on optimal speed planning to minimize energy use, proposing convex methods considering detailed internal and external losses for battery electric vehicles (BEVs), and optimal speed planning integrated with optimal energy management for fuel cell hybrid electric vehicles (FCHEVs). The proposed framework in this thesis is accurate while maintaining a low computational effort, which are the desired criteria for real-time algorithms. | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Meshginqalam_Ata_2022Aug_PHD.pdf | 6.13 MB | Adobe PDF | View/Open |
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