Component Sizing of Parallel Hybrid Electric Vehicle Using Optimal Search Algorithm
In designing a parallel hybrid electric vehicle, it is essential to select the optimal capacity of power sources and the optimal gear ratio of the torque coupler. The capacity of the power sources and the gear ratio of the torque coupler should be optimized simultaneously. However, since this process is excessively time-consuming, previous studies have selected the gear ratio of the torque coupler and then selected the capacity of power source. However, this approach cannot guarantee global optimization. In this paper, a feasible region is defined to satisfy the required performance of vehicle such as maximum speed, hill-climbing. and feasible points are selected inside the feasible region. In the conventional technique, the global optimal solution is obtained by simulating all feasible points. In the optimization technique, optimal points are simulated within the feasible region using several optimal search algorithms, such as the golden section search algorithm and the hillclimbing search algorithm. And using these algorithms, the number of simulations is reduced and the capacity of the power source and the gear ratio of the torque coupler are optimized simultaneously. Finally, the validity of the component sizing results is verified by comparing the global optimal solution obtained by applying the conventional technique with the solution obtained by applying the proposed optimization technique.
Key WordsHybrid electric vehicle Optimization Component sizing Golden section search algorithm Hill-climbing search algorithm
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- Chapra, S. C. and Canale, R. P. (2014). Numerical Methods for Engineers. 7th edn. McGraw-Hill. New York, USA.Google Scholar
- Ehsani, M., Gao, Y. and Emadi, A. (2010). Modern Electric, Hybrid Electric, and Fuel Cell Vehicles: Fundamentals, Theory, and Design. 2nd edn. CRC Press. New York, USA.Google Scholar
- Guzzella, L. and Sciarretta, A. (2007). Vehicle Propulsion System: Introduction to Modeling and Optimization. 2nd edn. Springer. New York, USA.Google Scholar
- Russel, S. and Norvig, P. (2010). Artificial Intelligence: A Modern Approach. 3rd edn. Pearson. London, UK.Google Scholar