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Energies 2018, 11(1), 89; doi:10.3390/en11010089

Development of an Advanced Rule-Based Control Strategy for a PHEV Using Machine Learning

School of Mechanical Engineering, Sungkyunkwan University, Seobu-ro, Suwon-si 2066, Korea
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Received: 18 December 2017 / Revised: 29 December 2017 / Accepted: 29 December 2017 / Published: 1 January 2018
(This article belongs to the Section Electrical Power and Energy System)
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Abstract

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. View Full-Text
Keywords: plug-in hybrid electric vehicle (PHEV); operating mode; driving cycle characteristics; battery state of charge (SOC); machine learning; rule-based control plug-in hybrid electric vehicle (PHEV); operating mode; driving cycle characteristics; battery state of charge (SOC); machine learning; rule-based control
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Son, H.; Kim, H.; Hwang, S.; Kim, H. Development of an Advanced Rule-Based Control Strategy for a PHEV Using Machine Learning. Energies 2018, 11, 89.

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