RESUMO
The increasing use of low-cost lithium iron phosphate cathodes in low-end electric vehicles has sparked interest in Prussian blue analogues (PBAs) for lithium-ion batteries. A major challenge with iron hexacyanoferrate (FeHCFe), particularly in lithium-ion systems, is its slow kinetics in organic electrolytes and valence state inactivation in aqueous ones. We have addressed these issues by developing a polymeric cathode electrolyte interphase (CEI) layer through a ring-opening reaction of ethylene carbonate triggered by OH- radicals from structural water. This facile approach considerably mitigates the sluggish electrochemical kinetics typically observed in organic electrolytes. As a result, FeHCFe has achieved a specific capacity of 125 mAh g-1 with a stable lifetime over 500 cycles, thanks to the effective activation of Fe low-spin states and the structural integrity of the CEI layers. These advancements shed light on the potential of PBAs to be viable, durable, and efficient cathode materials for commercial use.
RESUMO
This study introduces a fault diagnosis algorithm based on particle filtering for open-cycle liquid-propellant rocket engines (LPREs). The algorithm serves as a model-based method for the startup process, accounting for more than 30% of engine failures. Similar to the previous fault detection and diagnosis (FDD) algorithm for the startup process, the algorithm in this study is composed of a nonlinear filter to generate residuals, a residual analysis, and a multiple-model (MM) approach to detect and diagnose faults from the residuals. In contrast to the previous study, this study makes use of the modified cumulative sum (CUSUM) algorithm, widely used in change-detection monitoring, and a particle filter (PF), which is theoretically the most accurate nonlinear filter. The algorithm is confirmed numerically using the CUSUM and MM methods. Subsequently, the FDD algorithm is compared with an algorithm from a previous study using a Monte Carlo simulation. Through a comparative analysis of algorithmic performance, this study demonstrates that the current PF-based FDD algorithm outperforms the algorithm based on other nonlinear filters.