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

Hanho Son, Hyunhwa Kim, Sungho Hwang and Hyunsoo Kim
Additional contact information
Hanho Son: School of Mechanical Engineering, Sungkyunkwan University, Seobu-ro, Suwon-si 2066, Korea
Hyunhwa Kim: School of Mechanical Engineering, Sungkyunkwan University, Seobu-ro, Suwon-si 2066, Korea
Sungho Hwang: School of Mechanical Engineering, Sungkyunkwan University, Seobu-ro, Suwon-si 2066, Korea
Hyunsoo Kim: School of Mechanical Engineering, Sungkyunkwan University, Seobu-ro, Suwon-si 2066, Korea

Energies, 2018, vol. 11, issue 1, 1-15

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.

Keywords: plug-in hybrid electric vehicle (PHEV); operating mode; driving cycle characteristics; battery state of charge (SOC); machine learning; rule-based control (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 references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/11/1/89/pdf (application/pdf)
https://www.mdpi.com/1996-1073/11/1/89/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:11:y:2018:i:1:p:89-:d:125031

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().