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. / Thesis / Doctor of Philosophy (PhD) / 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.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/27754 |
| Date | January 2022 |
| Creators | Meshginqalam, Ata |
| Contributors | Bauman, Jennifer, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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