Supercapacitors (SCs), also known as ultracapacitors or electrochemical double-layer capacitors (EDLCs), have emerged as a remarkable class of energy storage devices that bridge the gap between conventional capacitors and batteries. These devices exhibit exceptional power density and long lifecycle, making them well-suited for a wide range of applications, from powering portable electronics to enabling rapid energy storage and release in various industrial systems. Unlike batteries, SCs store energy through the physical separation of charges at the electrode-electrolyte interface, leading to rapid charging and discharging capabilities. However, SCs are not without their challenges, notably leakage current and self-discharge, which can impact their long-term performance and practical utility. As the demand for energy-efficient and responsive power solutions intensifies, a thorough understanding of SCs’ behavior, coupled with accurate modeling techniques, becomes imperative. This thesis delves into this intricate realm, offering insights, models, and practical applications that collectively contribute to harnessing the potential of SCs across diverse domains.

In the realm of energy storage and power management, this thesis presents a cohesive exploration of SCs’ behavior and its practical implications through a series of five interrelated research papers. Focusing on the context of charging and discharging within series-connected SC modules under varying load conditions, the research advances an innovative exponential model that elegantly captures complex behaviors with less than 4% simulation error over extended time frames (31 days).

The initial study introduces an improved exponential equivalent circuit model (ECM) that elegantly characterizes the charging and discharging dynamics of series-connected SC modules. Leveraging a single-variable leakage resistance (VLR) approach, the model adeptly accounts for diverse self-discharge mechanisms. Unlike existing literature ECMs, this ECM’s simplicity and accuracy render it suitable for real-world applications in both short and long terms. The investigation extends to the modeling of multiple SC energy storage modules, providing insights into the behavior of SCs within varying configurations.

Expanding into the domain of Internet of Things (IoT) applications, the research highlights the significance of energy storage devices for wireless sensor nodes. Acknowledging the limitations of traditional batteries, the study advocates for SCs as a viable solution. A refined exponential model is then proposed as a novel approach to predict the discharge behavior of disposable printed flexible SCs, ensuring concordance with experimental findings. This approach involves employing an innovative method to model the non-linearity of self-discharge in printed SCs, effectively capturing this phenomenon. This ECM’s adaptability and alignment with measured self-discharge results offer a promising avenue for optimal IoT device performance.

Confronting the challenges of leakage current and self-discharge in SCs, the thesis presents a comprehensive framework. By proposing practical exponential ECMs, the study encapsulates nonlinear leakage and self-discharge phenomena. The empirical basis of these ECMs allows accurate prediction of discharge behaviors over extended periods, thereby holding potential for widespread practical application. A linear correlation was identified among the variables governing the exponential function of the equivalent parallel resistance (EPR) within the SC’s ECMs and the capacitance. The precision of the proposed ECMs was substantiated over an extended duration of 31 days, employing a diverse array of four distinct methodologies.

The thesis also takes a statistical turn by conducting a meticulous analysis of experimental parameters across printed SCs. Employing established ECMs, the research unveils statistical distributions and correlations, empowering safer operation, and more informed decision-making. Monte Carlo simulation technique unveils the long-term performance of SCs, offering insights into consistency and aiding in risk assessment. The conducted statistical analysis has revealed a normal distribution pattern for all the parameters characterizing the printed SCs. Additionally, this thesis presents a methodology to ascertain the upper limit of potential standard deviation (std) in capacitance values across SCs within a module, aiming to ensure the seamless operation of the module without encountering malfunctions. Furthermore, an observed linear correlation has been established between the maximum potential std of capacitance values among SCs and the cumulative voltage stored within the module.

Finally, the exploration expands to the activation of irreversible visual indicators (IVIs) through printed SCs, highlighting the potential of diverse monomer systems. The interplay of activation potential, coloration efficiency, and initial voltage underscores the feasibility of fully activating IVIs through series-connected SCs.

In summary, this thesis intricately weaves together five research papers to construct a comprehensive narrative about the behavior, modeling, and application of SCs. From exponential models to statistical analyses and practical implementations, this work contributes to the broader understanding of SC dynamics and their potential within contemporary energy storage systems and IoT applications. The results-driven approach solidifies SCs' impact as a versatile energy storage device, emphasizing realworld performance, and evidence-based decisions.