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An experimental study was performed to investigate the effect of fuel composition on combustion, gaseous emissions, and detailed chemical composition and size distributions of diesel particulate matter (PM) in a modern heavy-duty diesel engine with the use of the enhanced full-dilution tunnel system of the Engine Research Center (ERC) of the UW-Madison. Detailed description of this system can be found in our previous reports [1,2]. The experiments were carried out on a single-cylinder 2.3-liter D.I. diesel engine equipped with an electronically controlled unit injection system. The operating conditions of the engine followed the California Air Resources Board (CARB) 8-mode test cycle. The fuels used in the current study include baseline No. 2 diesel (Fuel A: sulfur content = 352 ppm), ultra low sulfur diesel (Fuel B: sulfur content = 14 ppm), and Fisher-Tropsch (F-T) diesel (sulfur content = 0 ppm).

An experimental study was carried out to investigate the effects of fuel injection timing on detailed chemical composition and size distributions of diesel particulate matter (PM) and regulated gaseous emissions in a modern heavy-duty D.I. diesel engine. These measurements were made for two different diesel fuels: No. 2 diesel (Fuel A) and ultra low sulfur diesel (Fuel B). A single-cylinder 2.3-liter D.I. diesel engine equipped with an electronically controlled unit injection system was used in the experiments. PM measurements were made with an enhanced full-dilution tunnel system at the Engine Research Center (ERC) of the University of Wisconsin-Madison (UW-Madison) [1, 2]. The engine was run under 2 selected modes (25% and 75% loads at 1200 rpm) of the California Air Resources Board (CARB) 8-mode test cycle.

Lithium-ion and Lithium polymer batteries are fast becoming ubiquitous in high-discharge rate applications for military and non-military systems. Applications such as small aerial vehicles and energy transfer systems can often function at C-rates greater than 1. To maximize system endurance and battery health, there is a need for models capable of precisely estimating the battery state-of-charge (SoC) under all temperature and loading conditions. However, the ability to perform state estimation consistently and accurately to within 1% error has remained unsolved. Doing so can offer enhanced endurance, safety, reliability, and planning, and additionally, simplify energy management. Therefore, the work presented in this paper aims to study and develop experimentally validated mathematical models capable of high-accuracy battery SoC estimation.