Expander Technologies for Engine Organic Rankine Cycle Applications

Abstract

The strive towards ever increasing automotive engine efficiencies for both diesel and gasoline engines has in recent years been forced by ever-stringent emissions regulations, as well as the introduction of fuel consumption regulations. The untapped availability of waste heat in the internal combustion engine (ICE) exhaust and coolant systems has become a very attractive focus of research attention by industry and academia alike. Even state of the art diesel engines operating at their optimum lose approximately 50% of their fuel energy in the form of heat. As a result, waste heat recovery (WHR) systems have gained popularity as they can deliver a reduction in fuel consumption and associated CO2 emissions. Of these, the Organic Rankine Cycle (ORC) is a well matured waste heat recovery technology that can be applied in vehicle powertrains, mainly due to the low additional exhaust backpressure on the engine and the potential opportunity to utilize various engine waste heat sources. ORCs have attracted high interest again recently but without commercial exploitation as of today due to the significant on-cost they represent to the engine and vehicle. In ORCs, expansion machines are the interface where useable power production takes place; therefore, selection of the expander technology is directly related to the thermal efficiency of the system. Moreover, the cost of the expander-generator units accounts for the largest proportion of the total cost. Therefore, selection of the most appropriate expander is of great importance at the early stage of any automotive powertrain project. This study aims to review the relevant research studies for expansion machines in ORC-ICE applications, analyzing the effects of specific speed on expander selection, exploring the operational characteristics of each expander to further assist in the selection of the most appropriate expander, and comparing the costs of various expanders based on publically available data and correlations.