Estimation of CO2 reduction by parallel hard-type power hybridization for gasoline and diesel vehicles

Yunjung Oh; Junhong Park; Jong Tae Lee; Jigu Seo; Sungwook Park

doi:10.1016/j.scitotenv.2017.03.171

Estimation of CO₂ reduction by parallel hard-type power hybridization for gasoline and diesel vehicles

Sci Total Environ. 2017 Oct 1:595:2-12. doi: 10.1016/j.scitotenv.2017.03.171. Epub 2017 Mar 31.

Authors

Yunjung Oh¹, Junhong Park², Jong Tae Lee², Jigu Seo¹, Sungwook Park³

Affiliations

¹ Graduated School of Hanyang University, Seoul 133-791, Republic of Korea.
² National Institute of Environmental Research, Incheon 404-708, Republic of Korea.
³ School of Mechanical Engineering, Hanyang University, Seoul 133-791, Republic of Korea. Electronic address: parks@hanyang.ac.kr.

PMID: 28365459
DOI: 10.1016/j.scitotenv.2017.03.171

Abstract

The purpose of this study is to investigate possible improvements in ICEVs by implementing fuzzy logic-based parallel hard-type power hybrid systems. Two types of conventional ICEVs (gasoline and diesel) and two types of HEVs (gasoline-electric, diesel electric) were generated using vehicle and powertrain simulation tools and a Matlab-Simulink application programming interface. For gasoline and gasoline-electric HEV vehicles, the prediction accuracy for four types of LDV models was validated by conducting comparative analysis with the chassis dynamometer and OBD test data. The predicted results show strong correlation with the test data. The operating points of internal combustion engines and electric motors are well controlled in the high efficiency region and battery SOC was well controlled within ±1.6%. However, for diesel vehicles, we generated virtual diesel-electric HEV vehicle because there is no available vehicles with similar engine and vehicle specifications with ICE vehicle. Using a fuzzy logic-based parallel hybrid system in conventional ICEVs demonstrated that HEVs showed superior performance in terms of fuel consumption and CO₂ emission in most driving modes.

Keywords: CO(2) emission rate; Fuel efficiency; Fuzzy logic; Hybrid electric vehicles (HEVs); Internal combustion engine vehicles (ICEVs); Vehicle dynamic based model.