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224 results on '"Chen, Hao-Sen"'

5. Theory of electrotuneable mechanical force of solid–liquid interfaces: A self-consistent treatment of short-range van der Waals forces and long-range electrostatic forces.

Author

Chen, Hai-Na, Yang, Le, Huang, Jun, Song, Wei-Li, and Chen, Hao-Sen

Subjects
VAN der Waals forces, ELECTROSTATIC interaction, IMMERSION in liquids, POLAR effects (Chemistry), METALLIC surfaces
Abstract

Elucidating the mechanical forces between two solid surfaces immersed in a communal liquid environment is crucial for understanding and controlling adhesion, friction, and electrochemistry in many technologies. Although traditional models can adequately describe long-range mechanical forces, they require substantial modifications in the nanometric region where electronic effects become important. A hybrid quantum–classical model is employed herein to investigate the separation-dependent disjoining pressure between two metal surfaces immersed in an electrolyte solution under potential control. We find that the pressure between surfaces transits from a long-range electrostatic interaction, attractive or repulsive depending on the charging conditions of surfaces, to a strong short-range van der Waals attraction and then an even strong Pauli repulsion due to the redistribution of electrons. The underlying mechanism of the transition, especially the attractive–repulsive one in the short-range region, is elucidated. This work contributes to the understanding of electrotunable friction and lubrication in a liquid environment. [ABSTRACT FROM AUTHOR]

Published
2024
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16. The Mechanism of Inhomogeneous Mass Transfer Process of Separators in Lithium‐Ion Batteries.

Author

Li, Na, Yin, Shuaimeng, Meng, Yufeng, Gu, Meirong, Feng, Zhenhe, Lyu, Siqi, Chen, Hao‐Sen, Song, Wei‐Li, and Jiao, Shuqiang

Subjects
LITHIUM-ion batteries, SERVICE life, ION migration & velocity, PHASE diagrams, TORTUOSITY, MASS transfer
Abstract

The liquid‐phase mass transport is the key factor affecting battery stability. The influencing mechanism of liquid‐phase mass transport in the separators is still not clear, the internal environment being a complex multi‐field during the service life of lithium‐ion batteries. The liquid‐phase mass transport in the separators is related to the microstructure of the separator and the physicochemical properties of electrolytes. Here, in‐situ local electrochemical impedance spectra were developed to investigate local inhomogeneities in the mass transfer process of lithium‐ion batteries. The geometric microstructure of the separator significantly impacts the mass transfer process, with a reduction in porosity leading to increased overpotentials. A competitive relationship among porosity, tortuosity, and membrane thickness in the geometric parameters of the separator were established, resulting in a peak of polarization. The resistance of the liquid‐phase mass transfer process is positively correlated with the viscosity of the electrolyte, hindering ion migration due to high viscosity. Polarization is closely related to the electrochemical performance, so a phase diagram of battery performance and inhomogeneous mass transfer was developed to guide the design of the battery. This study provides a foundation for the development of high stability lithium‐ion batteries. [ABSTRACT FROM AUTHOR]

Published
2024
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46. Short Term Charging Data Based Battery State of Health and State of Charge Estimation Using Feature Pyramid

Author

Dou, Bowen, Hou, Shujuan, Li, Hai, Chen, Hao-sen, Wei, Zhongbao, and Sun, Lei

Abstract

Accurate battery states estimation is critical to the safe and stable operation of Li-ion batteries, and it is one of the fundamental functions of a battery management system (BMS). This paper proposes a novel deep learning framework called multi-timescale dual feature-based state estimation network (MFN) using extremely limited charging data to estimate three critical states simultaneously end-to-end: maximum capacity, the capacity at the beginning of charging, and the capacity at the end of charging. The core of the framework is the dual feature extraction module (DFM) and the multi-timescale information extraction module (MTM). First, DFM extracts independent and coupling features of external signals (voltage, current, temperature) in the time dimension, respectively, and then merges them. Based on this, MTM explores the health features of the external signals on different time scales and merges them. Finally, the three internal states are estimated using the mined depth features. Experimental results in the Oxford battery data show that the root mean square error (RMSE) for three internal states is less than 9.71 mAh, corresponding to 1.31% of the nominal capacity.

Published
2024
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