Dynamic emission analysis of a hydrogen/diesel dual-fuel engine using clustering method
Hossein Mehnatkesh, David Gordon, Charles Robert Koch
Abstract
Internal combustion engines play a crucial role in transportation and agriculture, yet diesel-powered heavy and medium duty freight vehicles are significant sources of CO 2 and other exhaust emissions. Retrofitting existing diesel engines to dual-fuel systems is a promising solution to reduce these emissions. Among various dual-fuel configurations, the hydrogen/diesel dual-fuel (HDDF) engine with port injection is particularly effective in lowering exhaust emissions with minimal modifications to the engine. A detailed emissions analysis of an HDDF engine controlled by a data-driven predictive controller is presented. This controller tracks a transient load profile and optimizes diesel and hydrogen injections while respecting engine durability constraints. A comparison with 100% diesel engine operation reveals that the HDDF engine reduced carbon dioxide, carbon monoxide, and non-methane hydrocarbon emissions by 57%, 75%, and 37%, and particulate matter (PM) emission of the HDDF is one seventh of the diesel. Despite these benefits, one challenge is elevated nitrogen oxides (NO x ) emissions at high loads due to the hot flame temperature. A k -means clustering method was utilized to categorize key parameters — indicated mean effective pressure (IMEP), NO x , PM , and hydrogen energy share (HES) — into three groups: low, medium, and high. Results indicated that maintaining the same NO x and PM levels for increasing load required engine operation adjustments of: increasing diesel injection timing, reducing HES to adjust NO x level, and increasing both HES and diesel start of pre-injection to adjust PM level. The data-driven predictive controller and its cost function are used to set this balance between hydrogen and diesel injections.