A Modified Exponential Equivalent Parallel Resistance (EPR) Model for Predicting Self-Discharge Behavior of Printed Flexible Supercapacitors
Pourkheirollah, Hamed; Keskinen, Jari; Lupo, Donald; Mäntysalo, Matti (2022)
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Typically, batteries are used to power interconnected Internet of Things (IoT) devices. Intermittent manual replacement of batteries or recharging them after complete depletion is one of their major disadvantages, which increases the cost of maintaining and restricts the large-scale use of devices. Considering the longevity of devices and battery limitations, and in order to achieve the integrated and efficient operation of IoT devices, the development of alternative power sources and power management strategies is inevitable. The supercapacitor is a suitable energy storage option for energy-harvesting powered autonomous wireless sensor nodes in IoT applications. The leakage current value provided for the supercapacitors by the manufacturers is tested after the supercapacitor has been floated at a constant voltage for a long time. This raises concerns about the uncertainty of dynamic leakage current behavior during repeated charging and discharging of the supercapacitor in IoT applications. At present, there is no effective method to estimate and predict leakage current and the discharging behavior of supercapacitors in IoT applications with the aim of achieving optimal performance. In this work, an improved simplified exponential model is presented in order to simulate the non-linear discharge behavior of our fabricated printed flexible supercapacitors in long-term (31 days). The printed supercapacitors are disposable and have been fabricated using low-cost and non-toxic processes and materials. The model proposed in this work is very well adapted to the experimentally measured self-discharge results of the supercapacitors. In addition, according to the experimental and data fitting results of 10 fabricated supercapacitors, all the parameters defined in this model show good statistical values and have a Gaussian (normal) distribution.
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