Abstract: A vehicle automated unloading system may include a fill model and an unloading controller. The fill model is a model of a fill characteristic of a container as a function of variables comprising material unloading times, material unloading rates and material unloading locations. The unloading controller is to (a) determine a current model-based fill characteristic of the container using the dynamic fill model and (b) output control signals to adjust at least one of a material unloading time, a material unloading rate and a material unloading location based upon the current model-based fill characteristic of the container.

Abstract: A vehicle automated unloading system may include a fill model and an unloading controller. The fill model is a model of a fill characteristic of a container as a function of variables comprising material unloading times, material unloading rates and material unloading locations. The unloading controller is to (a) determine a current model-based fill characteristic of the container using the dynamic fill model and (b) output control signals to adjust at least one of a material unloading time, a material unloading rate and a material unloading location based upon the current model-based fill characteristic of the container.

Abstract: Improving tradeoffs between noise, fuel consumption, and emissions in future diesel engines are facilitated by the development of increasingly flexible fuel injection systems which can deliver more complex injection profiles. Piezoelectric injectors have the ability to deliver multiple, tightly spaced injections in each cycle. Closed-loop control is useful for this technology, and is assisted by on-line estimation of the injected fuel flow rate to be realized. Estimator results are compared against both open-loop simulation and experimental data for a variety of profiles at different rail pressures, and show improvement, particularly for more complex multi-pulse profiles. Internal states of the estimator are used to evaluate pulse-to-pulse interaction phenomena. Some embodiments include the use of estimations of actual transient fuel pulses, and the use of such estimations to achieve closed-loop control of the quantity of fuel injected in a pulse, and the dwell time between adjacent fuel pulses.

Abstract: A closed-loop control algorithm that reduces the increases in nitrogen oxides (NOx) commonly observed with biodiesel combustion while retaining particulate matter (PM) reductions with variable biodiesel blend fractions. One embodiment includes a control algorithm that is closed-loop with regards to combustible oxygen mass fraction (COMF) instead of exhaust gas recirculation (EGR) fraction. Yet another algorithm includes biodiesel blend estimation and “fuel-flexible” accommodation. A physics-based model has also been developed which predicts experimentally observed engine performance and emissions for biodiesel.

Abstract: While the materials compatibility challenges have largely been met in “flex-fuel” vehicles, the engine and aftertreatment operation has not been optimized as function of fuel type (i.e. ethanol, biodiesel, etc.). The full-scale introduction of alternative fuels is most likely going to occur as blends with conventional fuels. This is seen to some extend with the limited introduction of E85 (85% ethanol, 15% gasoline) and B20 (20% biodiesel, 80% conventional diesel.). This further exacerbates the challenge of accommodating variable fuel properties, as there will be differences in combustion properties due to both the type of alternative fuel (i.e. pure biodiesel vs. pure diesel) and blend ratio (i.e. B20 vs. B80). Real-time estimation of the fuel blend is key to the optimized use of two-component fuels (e.g. diesel-biodiesel, gasoline-ethanol, etc.). The approach outlined here uses knowledge of the exhaust composition, fuel and air delivery rates to the engine to estimate the fuel blend.