An Adaptive Energy Management Strategy for Simultaneous Long Life and High Wake‐Up Success Rate of Wireless Sensor Network Nodes. Issue 10 (14th August 2021)

• Record Type: Journal Article
• Authors: Dai, Keren; Zhang, Cheng; Li, Qingyu; Huang, Xiaohua; Zhang, He
• Is Part Of: Energy technology. Volume 9:Issue 10(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-08-14
• Subjects: energy management strategies -- tight energy supplies -- hybrid energy storage -- wireless sensor networks; Energy development -- Periodicals; Power resources -- Periodicals; 333.79
• DOI: 10.1002/ente.202100522
• Languages: English
• ISSNs: 2194-4288

Abstract

Abstract : For wireless sensor network (WSN) nodes with an extremely tight energy supply, herein, an adaptive energy management strategy based on a hybrid energy system composed of a photovoltaic device, Li battery, and supercapacitor is proposed. According to information fusion of the irradiation intensity, load consumption and the state of charge of the battery and supercapacitor, the estimation of energy shortage caused by future wake‐up pulses is carried out. Based on this, the charge/predischarge power of the Li battery is adaptively adjusted. Simulations validate that much lower Li battery capacity loss than the previous strategies is achieved without sacrificing the wake‐up success rate, thereby extending the life of the hybrid energy module in a WSN node by an order of magnitude. In addition, the proposed strategy has excellent robustness in the double‐random environmental and working conditions including the irradiation intensity, driving event frequency, etc., and can always maintain excellent performance while these parameters are changing within a wide range. Therefore, an effective energy guarantee for long life and high wake‐up success rate of WSN nodes under double‐random environmental and working conditions is provided and the practical application of WSN nodes in such scenarios is promoted. Abstract : A synergic management strategy for a hybrid energy system in double‐random environmental and working conditions of the irradiation intensity and driving event frequency is presented, with a parameter optimization scheme avoiding large computation via typical training data, resulting in a robust long battery life and high wake‐up success rate in variable harsh conditions.