A backpropagation neural network-based hybrid energy recognition and management system

Xiwen Zhu, Mingxue Li, Xiaoqiang Liu and Yufeng Zhang

Energy, 2024, vol. 297, issue C

Abstract: For several decades, small electronic devices like wireless sensor network nodes (WSNs) tend to be powered by ambient energy, and the multi-input energy platform attracts much attention because sensors are usually used in complicated surroundings. However, for multi-input energy platform energy management is complex and the demand of the consumers is stochastic. To solve the problems, this paper presents a backpropagation neural network (BPNN) based hybrid energy recognition and management System (ERMS). The design applies artificial intelligence algorithms to energy forecasting recognition. And it achieves energy-matching management according to recognition results. Besides, we implemented the energy recognition algorithm on an application specific integrated circuit (ASIC) innovatively, which is manufactured in a standard 180 nm CMOS technology. The energy recognition chip area is 1.45mm × 1.45 mm. The experimental data present that the system can identify different types of input energy and control the energy flows automatically. The current consumption of the ASIC is 65μA at 1 MHz and the recognition accuracy can reach 98 %. Moreover, the hybrid energy recognition and management system platform worked effectively. The measurement results show that the power conversion efficiency of the system to photovoltaic energy input is 85 %. Furthermore, when the input is piezoelectric energy, the power management system output power can achieve 7.4 mW.

Keywords: Backpropagation neural network (BPNN); Energy recognition; ASIC; Energy harvesting; Automatic energy management (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:297:y:2024:i:c:s0360544224010375

DOI: 10.1016/j.energy.2024.131264

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