Energy management system for controlling series hybrid electric motorcycle
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Date
2016-01-01
Authors
Cham Chin Long
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Abstract
Pollution issues and scarcity of fossil fuel inspire the development of hybrid
electric vehicle. Motorcycles are widely used in developing countries and Asia for
their size, cost, and maneuverability. They create enormous pollutants due to the lack
of viable pollution prevention technologies. There are plenty of research on hybrid
cars, but very limited literature on hybrid motorcycle, thus, the behavior and
performance of hybrid motorcycle are not completely known. Hybridizing
conventional motorcycle is necessary because of the increasing usage due to the
population growth and rising living standard and these can bring about disastrous
climate change if current habit persisted. One of the problems that remain unsolved
in hybrid motorcycle is the prediction of the future trip. Various techniques have
been used for the prediction, but these are either too complex, expensive, or
performed poorly. This research improves the performance of an electric motorcycle
by hybridization where the performance of the building blocks for hybrid motorcycle
were studied and characterized. Via dynamic programming simulation, efficient use
of hybrid motorcycle was found. The characteristics identified from the dynamic
programming were then used for the formulation of the energy management system.
Kalman filtering was applied to the energy management system to pretreat the
signals measured from the traffic. Kalman filter requires only 2 kB when
implemented with Atmel ATmega328p compared to 10 kB required by simple
moving average filter. The series hybrid electric motorcycle embedded with the
energy management system achieves 89.58 km per charging compared to 19.30 km
per charging for the electric motorcycle under the modified ECE-R40 drive cycle. In
addition, the energy management system outperformed the conventional thermostat
control strategy in terms of traveling distance and it has more optimized fuel usage.
The energy management system proposed achieves above 80 % performance of the
dynamic programming approach, for long traveling distance, it achieves as high as
98.06 %. Tuning and adaptation of the control algorithm had been demonstrated so
developers can make use of them for their applications. Several contributions are
made: electromagnetic torque of brushless DC motor can be estimated based on the
single-phase current sensing. The mathematical models developed for subsystem
components and the experimental techniques are invaluable for hybrid motorcycle
developers. Besides, efficient series hybrid electric motorcycle performance is
obtainable with simple and efficient control algorithm developed.