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Energies 2017, 10(3), 404; doi:10.3390/en10030404

Energy Management of Parallel-Connected Cells in Electric Vehicles Based on Fuzzy Logic Control

State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
School of Mechanical Science and Engineering, Jilin University, Changchun 130022, China
College of Automotive Engineering, Jilin University, Changchun 130022, China
Author to whom correspondence should be addressed.
Academic Editor: Jih-Sheng (Jason) Lai
Received: 24 December 2016 / Revised: 13 March 2017 / Accepted: 17 March 2017 / Published: 21 March 2017
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Inconsistencies that are associated with parallel-connected cells used in electric vehicles induce varied states of charge (SOCs) in each cell. Thus, loop current in the battery pack is inevitable, and this reduces overall capacity, energy utilization rate, and pack lifetime. However, no method is available to address loop current. To reduce loop current and the resulting battery inconsistency, a parallel-connected cell pack (PCCP) model that considers thermal effects is established, and a novel Simscape model that is based on PCCP is successfully constructed. Furthermore, the strategy of parallel-connected cell energy management (PCCEM) is proposed to utilize fuzzy logic control (FLC) strategy, which automatically adjusts the number of cells in a circuit in accordance with the load demand, and turns on the first N switches in the corresponding SOC order. The New European Driving Cycle (NEDC) driving cycle simulation shows that the PCCEM strategy considerably reduces loop current and improves the consistency of battery performance and the utilization rate of battery power. View Full-Text
Keywords: parallel-connected cells; loop current; electric vehicle; energy management; fuzzy logic parallel-connected cells; loop current; electric vehicle; energy management; fuzzy logic

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Song, C.; Shao, Y.; Song, S.; Chang, C.; Zhou, F.; Peng, S.; Xiao, F. Energy Management of Parallel-Connected Cells in Electric Vehicles Based on Fuzzy Logic Control. Energies 2017, 10, 404.

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