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Control strategy optimization for hybrid electric vehicle based on particle swarm and simulated annealing algorithm

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5 Author(s)
Keliang Chen ; College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China ; Yuanwang Deng ; Fei Zhou ; Guixian Sun
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In order to further reduce the fuel consumption and emissions of the parallel hybrid electric vehicle, first of all, the multi-objective optimization problem is converted into single-objective optimization problem. Then logic threshold control parameters are optimized with the particle swarm and simulated annealing algorithm(PSOSA). The optimized control strategy is separately used for three different test drive cycles (UDDC, EUDC and JA1015) and finally the optimized fuel consumption and emissions are compared with which is not optimized. The results show that FC, HC, CO and NOx are separately decreased by 14.67%, 10.72%, 33.10%, 20.17% in UDDC test drive cycle; FC, HC, CO are separately decreased by 9.68% 1.00%, 33.87% but NOx is increased by 18.69% in EUDC test drive cycle; FC, HC, CO and NOx are separately decreased by 19.05%, 8.98%, 3.16%, 25.41% in JA1015 test drive cycle.

Published in:

Electric Information and Control Engineering (ICEICE), 2011 International Conference on

Date of Conference:

15-17 April 2011