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Wang, Aiping

Publications and source records attributed to Wang, Aiping.

Revisiting the initial irreversible capacity loss of LiNi 0.6 Co 0.2 Mn 0.2 O 2 cathode material batteries

Layered LiNi 0.6 Co 0.2 Mn 0.2 O 2 (NCM622) attracts widespread attention primarily due to its potential for high energy density and moderate thermal stability. However, the low initial coulombic efficiency (ICE) of the material limits the maximum utilization of their capacity. The capacity loss in the first cycle occurs under 4.0V and keep almost constant are considered as common characteristics for NCM-based materials. A clear cognition on the initial capacity loss may light the way to improve the practical reversible capacity of NCM622 at 4.0V. Conducting operando X-ray diffraction during galvanostatic charge/discharge cycling at different temperature (25 degrees C, 45 degrees C and 60 degrees C) and different current (0.1C, 0.01C, 1C=120 mA g -1 ) in the voltage range of 2.7-4.0V, we find that only 8% of the measured initial irreversible capacity loss is associated with parasitic reactions that form cathode/electrolyte interface, and that the dominant contributors include the slow Li + diffusion kinetics (similar to 46% contribution) and irreversible O3/H1-3 phase transition (similar to 46% contribution). Finally, this semi-quantitative study provides new insight on initial capacity loss, guiding further targeted modification and fully utilization of NCM622.

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Survey on stochastic distribution systems: A full probability density function control theory with potential applications

Complex systems seen either in general engineering practice or economics are subjected to ever increased uncertainties that are mostly represented as random variables or parameters, and the characteristics of random variables are represented by their probability density functions (PDFs). Controlling their PDFs means to shape their stochastic distributions and in general it would provide a full treatment for system analysis and operational control and optimization. This leads to the development of stochastic distribution control (SDC) systems theory in the past decades, where the original aim of the controller design is to realize a shape control of the distributions of certain random variables in their PDFs sense for some engineering processes. Indeed, once the PDFs of these random variables or parameters are used to describe their distribution characters, the control task is to obtain control signals so that the output PDFs of stochastic systems are made to follow their target PDFs. The subject of SDC was initially originated for non-Gaussian stochastic control systems design but has found a wide spectrum of applications in general systems in terms of data-driven modeling, analysis, signal processing (filtering), data mining via multivariable statistics, decision-making (optimization) for systems subjected to uncertainties and even in economics. In this context, SDC constitutes an effective primer tool for complex system analysis, control and operational optimizations. In this review paper, a detailed survey of the developments on the research of SDC systems will be made together with their wide spectrum applications and future perspectives.

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