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    Publication Date: 2018-01-02
    Description: Energies, Vol. 11, Pages 89: Development of an Advanced Rule-Based Control Strategy for a PHEV Using Machine Learning Energies doi: 10.3390/en11010089 Authors: Hanho Son Hyunhwa Kim Sungho Hwang Hyunsoo Kim This paper presents an advanced rule-based mode control strategy (ARBC) for a plug-in hybrid electric vehicle (PHEV) considering the driving cycle characteristics and present battery state of charge (SOC). Using dynamic programming (DP) results, the behavior of the optimal operating mode was investigated for city (UDDS×2, JC08 ×2) and highway (HWFET ×2, NEDC ×2) driving cycles. It was found that the operating mode selection varies according to the driving cycle characteristics and battery SOC. To consider these characteristics, a predictive mode control map was developed using the machine learning algorithm, and ARBC was proposed, which can be implemented in real-time environments. The performance of ARBC was evaluated by comparing it with rule-based mode control (RBC), which is a CD-CS mode control strategy. It was found that the equivalent fuel economy of ARBC was improved by 1.9–3.3% by selecting the proper operating mode from the viewpoint of system efficiency for the whole driving cycle, regardless of the battery SOC.
    Electronic ISSN: 1996-1073
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Published by MDPI Publishing
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