Genetic Algorithm based optimal component sizing for an electric vehicle
- Publication Type:
- Conference Proceeding
- Citation:
- IECON Proceedings (Industrial Electronics Conference), 2013, pp. 7331 - 7336
- Issue Date:
- 2013-12-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
 | 2013001163OK.pdf | Published version | 222 kB |
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Electric vehicles (EVs) are one component in the pursuit of clean and sustainable energy sources. They allow clean electric energy to be utilized in transportation and reduce pollution in the urban environment. Hybrid Energy Storage Systems (HESS) can be utilized in EVs and these comprise of batteries and ultracapacitors. They allow for the full use of both the high energy density characteristic of the batteries and the high power density performance of the ultracapacitors to achieve a satisfying driving range while meeting transient power demands at an acceptable manufacturing cost. In this paper, component sizing is investigated as an optimization problem with the aim of minimizing the cost of the energy storage system. The problem is solved using a Genetic Algorithm (GA) for an example EV. In the implementation of the GA, the driving performance requirements are set as the constraints and formulated with penalty functions. This is because the GA is not appropriate for constrained optimization problems. In order to enhance the robustness of the sizing, three different driving cycles are incorporated into the optimization process. They are the NEDC, UDDS and CHINACITY cycles. The result is obtained and the effectiveness and reliability of the GA are further verified by implementing another optimization using the Particle Swarm Optimization (PSO) algorithm. © 2013 IEEE.
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