Paper
19 October 2023 Influence of operating temperature on self-discharge of built-in battery of smart electric energy meter
Yunfeng Zheng, Zhenwei Yang, Mingjie Cui, Wang Rao, Huiyang Yin
Author Affiliations +
Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127090J (2023) https://doi.org/10.1117/12.2685224
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China
Abstract
The built-in battery of smart electric energy meter is used to realize the functions of information storage, special event recording, clock normal operation and so on. It is the key component of the electric energy meter. However, in the field operation, a high proportion of smart electric energy meters occurred battery under-voltage warning, and these meters are in the service life required by the technical specifications. Research on this problem is carried out. The purpose is to find out the cause of battery under-voltage fault, and put forward improvement measures to improve the service life of smart electric energy meter. The hourly temperature in the measuring box at different installation positions is measured. It indicates that the electric energy meter in operation can be in high temperature for a long time. The capacity prediction model of the built-in battery of smart electric energy meter based on Arrhheninus formula and BP neural network was established. It was found that the self-discharge rate of the Li/SOCl2 battery would increase exponentially when the temperature increased.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yunfeng Zheng, Zhenwei Yang, Mingjie Cui, Wang Rao, and Huiyang Yin "Influence of operating temperature on self-discharge of built-in battery of smart electric energy meter", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127090J (19 October 2023); https://doi.org/10.1117/12.2685224
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KEYWORDS
Batteries

Data modeling

Temperature metrology

Education and training

Neural networks

Neurons

Performance modeling

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