Power reduction optimization with swarm based technique in electric power assist steering system
Energy management in electric vehicle technology is very important as the energy source of all its system operations are solely relying on the battery. Efforts are being made to reduce the energy consumed as much as possible in electric vehicle system. As one of the auxiliary elements of the syste...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English English English |
| Published: |
Elsevier Limited
2016
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/50072/7/50072_Power%20reduction%20optimization%20with%20swarm.pdf http://irep.iium.edu.my/50072/6/50072_Power%20reduction%20optimization%20with%20swarm_SCOPUS.pdf http://irep.iium.edu.my/50072/5/50072_Power%20reduction%20optimization%20with%20swarm_WOS.pdf http://irep.iium.edu.my/50072/ http://www.sciencedirect.com/science/article/pii/S0360544216301013 |
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| Summary: | Energy management in electric vehicle technology is very important as the energy source of all its
system operations are solely relying on the battery. Efforts are being made to reduce the energy
consumed as much as possible in electric vehicle system. As one of the auxiliary elements of the system,
the electric power assist steering system can be controlled or manipulated in such a way that minimum
energy from the battery source is being drawn during operation. This unique feature enables the system
to be tuned with the optimal performance setting so that less power is needed for its optimum operation.
The research's aim is to apply the swarm optimization technique; Particle Swarm Optimization and Ant
Colony Optimization to improve the controller's performance. The investigation covers an analysis of
power consumption for the system in simulation and experimental setup. Both simulation and experimental
tests are conducted to validate the proposed controller performance in optimizing power
reduction. It is proven that the ant colony optimization tuned controller outperform the controller tuning
using particle swarm optimization for power minimization. |
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