Combined Rule Based-Grey Wolf Optimization Energy Management Algorithm for Emission Reduction of Converted Plug-In Hybrid Electric Vehicle

Conversion of the conventional vehicle (CV) into the plug-in hybrid electric vehicle (PHEV) is one of the promising solutions to improve transport sustainability and reduce outdoor air pollution of current vehicles running on the road. The performance of PHEV depends on an energy management strategy (EMS) of a hybrid powertrain. The article presents a combined rule based-grey wolf optimization (RGWO) energy management approach to improve performance of rule-based control and to reduce complexity and computational load of optimal control approach for the converted plug-in hybrid electric vehicle (CPHEV). A diesel vehicle converted to parallel hybrid topology is used for the study. Fuel consumption and emissions, viz., nitrous oxide (NOx) and particulate matter (PM) are considered as performance parameters. The mode of operation is decided using rule-based strategy and if the internal combustion (IC) engine is operated, then the grey wolf optimization (GWO) algorithm is used to optimize specific fuel consumption (FC), NOx, and PM. In-depth performance analysis is carried out for two driving cycles (Indian Urban Driving Cycle [IUDC] and Indian Highway Driving Cycle [IHDC]). The NOx and PM reduction are observed to be 30.80% and 73.86% for a 100 km run with a proposed RGWO strategy for a CPHEV (with fully charged battery) compared to a conventional diesel vehicle tested for the IUDC. Moreover, for the IHDC, NOx and PM reduction is observed to be 75.29% and 48.21% for a CPHEV compared to conventional one. The results indicate that the proposed EMS for CPHEV topology keeps pollution under control as per required Bharat Stage (BS) norms.