Investigations on Conversion of Conventional Vehicle into Plug in Hybrid Electric Vehicle and Its Energy Management Strategy for Performance Improvement and Emission Reduction

Abstract

In a country like India, the majority of the vehicles running on the road are fossil fuel newlinebased and they do not comply with emission norms. In spite of continuous efforts to keep newlineemission under control with alternative fuels, emissions are not under control. Conversion of newlinethe conventional vehicle (CV) into the plug-in hybrid electric vehicle (PHEV) is one of the newlinepromising solutions to improve transport sustainability and reduce outdoor air pollution caused newlineby present vehicles running on the road. However, it is limited by conversion complexity, energy newlinemanagement strategy and economics. The work reported in this thesis begins with research on newlineconversion of diesel vehicle into the plug-in hybrid electric vehicle. Considering the practical newlinechallenges to convert CV into PHEV, the converted PHEV (CPHEV) is proposed with minimum newlinechange in the mechanical drivetrain component. Electric powertrain is designed by the newlinefundamental vehicle dynamic approach suitable for any route instead of conventional route newlinespecific approach. A complete architecture of CPHEV is proposed with different modes of newlineoperation. Investigations have been done in energy management strategies of converted PHEV. newlineCombined fuzzy and swarm intelligence based algorithm is proposed towards real-time newlineimplementation to reduce complexity and computational time of global optimization strategy. newlineFuzzy is used to decide the power split (decide operation of engine, motor or both) and if the newlineengine is required to be operated, then modified artificial bee colony (MABC) optimization is newlineused to decide the operating point of the engine to minimize all emissions and specific fuel newlineconsumption (SFC). All emissions viz. hydrocarbon (HC), carbon monoxide (CO), nitrogen oxide newline(NOx) and particulate matter (PM) are considered as optimization parameters with weighted newlinefactors. Offline engine maps of SFC, HC, CO, NOx, and PM are obtained for a 70 hp engine from newlineOak Ridge National Laboratory. Artificial bee colony (ABC) algorithm is modified in initialization newlineand scout phase to get