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

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State Key Laboratory of Mechanical Transmissions, School of Automotive Engineering, Chongqing University, Chongqing 400044, China
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Chongqing Changan Automobile Co., Ltd., Chongqing 400023, China
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Author to whom correspondence should be addressed.
Energies 2018, 11(6), 1531; https://doi.org/10.3390/en11061531
Received: 4 May 2018 / Revised: 28 May 2018 / Accepted: 8 June 2018 / Published: 12 June 2018
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%. View Full-Text
Keywords: HEV; energy management strategy; driving condition identification; fuel economy HEV; energy management strategy; driving condition identification; fuel economy
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MDPI and ACS Style

Li, S.; Hu, M.; Gong, C.; Zhan, S.; Qin, D. Energy Management Strategy for Hybrid Electric Vehicle Based on Driving Condition Identification Using KGA-Means. Energies 2018, 11, 1531.

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