Dynamic exhaust oxygen based biodiesel blend estimation with an Extended Kalman Filter

Biodiesel, an alternative fuel for diesel engines, has the potential to reduce dependence on foreign sources of petroleum, reduce harmful particulate matter (soot), carbon monoxide, and unburned hydrocarbon emissions, and net carbon dioxide emissions. Despite these benefits, biodiesel utilization has the combustion-related challenges of increased fuel consumption and increased nitrogen oxides emissions. These challenges may be mitigated through “fuel-flexible”, closed-loop control of the combustion process, however, that requires a practical means of ascertaining the biodiesel blend fraction. Previous work by the authors developed and demonstrated exhaust oxygen based blend estimation in steady-state. This work presents a method of dynamic blend estimation through the use of an Extended Kalman Filter (EKF). Experimental validation demonstrates both fast convergence and steadiness in the presence of uncertainties. This was accomplished using a time-varying measurement covariance matrix. Not only does the proposed approach estimate the biodiesel blend fraction well, but such estimation also does not preclude using exhaust oxygen information to improve the estimate of mixture fraction/air-fuel ratio. For some cases the approach outlined in this work allows for the practical, real-time, onboard biodiesel blend fraction estimation with no additional hardware.