There has been an increased necessity to search for alternative transportation methods,
mainly driven by limited fuel availability and the negative impacts of climate
change and exhaust emissions. These factors have lead to increased regulations and a
societal shift towards a cleaner and more e cient transportation system. Automotive
and technology companies need to be looking for ways to reshape mobility, enhance
safety, increase accessibility, and eliminate the ine ciencies of the current transportation
system in order to address such a shift. Hybrid vehicles are a popular solution
that address many of these goals. In order to fully realize the bene ts of hybrid vehicle
technology, the power distribution decision needs to be optimized. In the past,
global optimization techniques have been dismissed because they require knowledge
of the journey of the vehicle in advance, and are generally computationally extensive.
Recent advancements in technologies, like sensors, cameras, lidar, GPS, Internet of
Things, and computing processors, have changed the basic assumptions that were
made during the vehicle design process. In particular, it is becoming increasingly
possible to know future driving conditions. In addition to this, autonomous vehicle
technology is addressing many safety and e ciency concerns. This thesis considers and integrates recent technologies when de ning a new approach
to hybrid vehicle supervisory controller design and optimization. The dynamic
programming algorithm has been systematically applied to an autonomous
vehicle with a power-split hybrid electric powertrain. First, a more realistic driving
cycle, the Journey Mapping cycle, is introduced to test the performance of the
proposed control strategy under more appropriate conditions. Techniques such as
vectorization and partitioning are applied to improve the computational e ciency of
the dynamic programming algorithm, as it is applied to the hybrid vehicle energy
management problem. The dynamic programming control algorithm is benchmarked
against rule-based algorithms to substantively measure its bene ts. It is proven that
the DP solution improves vehicle performance by at least 9 to 17% when simulated
over standard drive cycles. In addition, the dynamic programming solution improves
vehicle performance by at least 32 to 39% when simulated over more realistic conditions.
The results emphasize the bene ts of optimal hybrid supervisory control and
the need to design and test vehicles over realistic driving conditions. Finally, the dynamic
programming solution is applied to the process of adaptive control calibration.
The particle swarm optimization algorithm is used to calibrate control variables to
match an existing controller's operation to the dynamic programming solution. / Thesis / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/23054 |
| Date | 05 1900 |
| Creators | Vadala, Brynn |
| Contributors | Emadi, Ali, Mechanical Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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