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

Shuxian Li,Datong Qin,Minghui Hu,Sen Zhan,Changchao Gong

doi:10.3390/en11061531

Shuxian Li, Datong Qin + Show 3 more

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https://doi.org/10.3390/en11061531

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Journal: Energies	Publication Date: Jun 12, 2018
Citations: 18	License type: CC BY 4.0

Affiliation: Chongqing University

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%.

Highlights

An hybrid electric vehicle (HEV) power system consists of multiple power sources
After conducting the optimization process outlined by the above three steps, vmean and rdrive are selected as the representative characteristic parameters
This study aims to achieve a practical energy management strategy based on driving condition identification, which can be readily applied to real vehicle controllers

Summary

Introduction

An hybrid electric vehicle (HEV) power system consists of multiple power sources. An appropriate energy management strategy coordinates the power system components, and achieves a reasonable distribution of the demanded power between multiple power sources. As a result, improved fuel economy can be attained along with satisfactory dynamic performance. The recently proposed adaptive energy management strategies present superior performance, but they are difficult to be implemented in real HEVs. research on a real-time energy management strategy based on driving condition identification is of great practical value and theoretical significance. Several adaptive energy management strategies have been proposed in the literature

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