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Open AccessArticle

Battery Pack Grouping and Capacity Improvement for Electric Vehicles Based on a Genetic Algorithm

1
Faculty of Transportation Engineering, Kunming University of Science of Technology, Kunming 650500, China
2
Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China
*
Author to whom correspondence should be addressed.
Academic Editor: Jih-Sheng (Jason) Lai
Energies 2017, 10(4), 439; https://doi.org/10.3390/en10040439
Received: 27 February 2017 / Revised: 20 March 2017 / Accepted: 22 March 2017 / Published: 31 March 2017
This paper proposes an optimal grouping method for battery packs of electric vehicles (EVs). Based on modeling the vehicle powertrain, analyzing the battery degradation performance and setting up the driving cycle of an EV, a genetic algorithm (GA) is applied to optimize the battery grouping topology with the objective of minimizing the total cost of ownership (TCO). The battery capacity and the serial and parallel amounts of the pack can thus be determined considering the influence of battery degradation. The results show that the optimized pack grouping can be solved by GA within around 9 min. Compared with the results of maximum discharge efficiency within a fixed lifetime, the proposed method can not only achieve a higher discharge efficiency, but also reduce the TCO by 2.29%. To enlarge the applications of the proposed method, the sensitivity to driving conditions is also analyzed to further prove the feasibility of the proposed method. View Full-Text
Keywords: battery pack grouping; electric vehicles (EVs); genetic algorithm (GA); total cost of ownership (TCO) battery pack grouping; electric vehicles (EVs); genetic algorithm (GA); total cost of ownership (TCO)
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MDPI and ACS Style

Chen, Z.; Guo, N.; Li, X.; Shen, J.; Xiao, R.; Li, S. Battery Pack Grouping and Capacity Improvement for Electric Vehicles Based on a Genetic Algorithm. Energies 2017, 10, 439.

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