Energy Management Strategy for Hybrid Electric Vehicle Based on Driving Condition Identification Using KGA-Means

Shuxian Li, Minghui Hu, Changchao Gong, Sen Zhan and Datong Qin
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Shuxian Li: State Key Laboratory of Mechanical Transmissions, School of Automotive Engineering, Chongqing University, Chongqing 400044, China
Minghui Hu: State Key Laboratory of Mechanical Transmissions, School of Automotive Engineering, Chongqing University, Chongqing 400044, China
Changchao Gong: State Key Laboratory of Mechanical Transmissions, School of Automotive Engineering, Chongqing University, Chongqing 400044, China
Sen Zhan: Chongqing Changan Automobile Co., Ltd., Chongqing 400023, China
Datong Qin: State Key Laboratory of Mechanical Transmissions, School of Automotive Engineering, Chongqing University, Chongqing 400044, China

Energies, 2018, vol. 11, issue 6, 1-16

Abstract: In order to solve the problem related to adaptive energy management strategies based on driving condition identification being difficult to be applied to a real hybrid electric vehicle (HEV) controller, this paper proposes an energy management strategy by combining the driving condition identification algorithm based on genetic optimized K-means clustering algorithm (KGA-means), and the equivalent consumption minimization strategy (ECMS). The simulation results show that compared with ECMS, the energy management strategy proposed in this article drives the engine working point closer to the best efficiency curve, and smooths out the state of charge (SOC) change and better maintains the SOC in a highly efficient area. As a result, the vehicle fuel consumption reduces by 6.84%.

Keywords: HEV; energy management strategy; driving condition identification; fuel economy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (21)

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