Supercapacitor Electro-Mathematical and Machine Learning Modelling for Low Power Applications

A comprehensive review on MoSe2 nanostructures with an overview of machine learning techniques for supercapacitor applications

In the past few decades, supercapacitors (SCs) have emerged as good and reliable energy storage devices due to their impressive power density, better charge-discharge rates, and high cycling stability. The main components of a supercapacitor are its electrode design and composition. Many compositions are tested for electrode preparations, which can provide good performance. Still, research is widely progressing in developing optimum high-performance electrodes. Metal chalcogenides have recently gained a lot of interest for application in supercapacitors due to their intriguing physical and chemical properties, unique crystal structures, tuneable interlayer spacings, broad oxidation states, etc. MoSe2, belonging to the family of Transition Metal Dichalcogenides (TMDs), has also been well explored recently for application in supercapacitors due to its similar properties to 2D materials. In this review, we briefly discuss supercapacitors and their classification. Various available synthesis routes for MoSe2 preparation are summarized. A detailed assessment of the electrochemical performances of different MoSe2 composites, including cyclic voltammetry (CV) analysis and galvanostatic charge-discharge (GCD) analysis, is given for symmetric and asymmetric supercapacitors. The limitations of MoSe2 and its composites are mentioned briefly. The use of machine learning methods and algorithms for supercapacitor applications is discussed for forecasting valuable details. Finally, a summary is provided, along with conclusions.

Modeling the Double Layer Capacitance Effect in Electrolyte Gated FETs with Gel and Aqueous Electrolytes

Potential implementation of bio-gel Electrolyte Double Layer capacitors (bio-gel EDLCs) and electrolyte-gated FET biosensors, two commonly reported configurations of bio-electrolytic electronic devices, requires a robust analysis of their complex internal capacitive behavior. Presently there is neither enough of the parameter extraction literature, nor an effective simulation model to represent the transient behavior of these systems. Our work aims to supplement present transient thin film transistor modelling techniques with the reported parameter extraction method, to accurately model both bio-gel EDLC and the aqueous electrolyte gated FET devices. Our parameter extraction method was tested with capacitors analogous to polymer-electrolyte gated FETs, electrolyte gated Field effect transistor (EGOFET) and Organic Electrolyte Gated Field Effect Transistor (OEGFET) capacitance stacks. Our method predicts the input/output electrical behavior of bio-gel EDLC and EGOFET devices far more accurately than conventional DLC techniques, with less than 5% error. It is also more effective in capturing the characteristic aqueous electrolyte charging behavior and maximum charging capability which are unique to these systems, than the conventional DLC Zubieta and the Two branch models. We believe this significant improvement in device simulation is a pivotal step towards further integration and commercial implementation of organic bio-electrolyte devices. The effective reproduction of the transient response of the OEGFET equivalent system also predicts the transient capacitive effects observed in our previously reported label-free OEGFET biosensor devices. This is the first parameter extraction method specifically designed for electrical parameter-based modelling of organic bio-electrolytic capacitor devices.

Technical Solutions of Forest Machine Hybridization

The paper deals with the characteristics of three different types of power train hybridization of forest logging machines and with the benefits of reducing environmental impacts by comparing new technology with more conventional, older technology. New hybridization options that could be implemented in forestry machines are also discussed. The paper divides a hybrid solution into three classes based on the energy used in the system of hybridization. First is an electro-hybrid system that uses an electric motor and battery or different storage device. The second, a hydraulic hybrid system, is a solution with a hydraulic accumulator, hydraulic motor, and pump. The third system is a combination of the electro-hybrid and hydraulic-hybrid system. The current technical and technological development of hybrid drive systems, as well as their components, has led to significant improvements in drive performance and thus better performance of the new generation of forest vehicles. Improved energy efficiency using hybrid propulsion systems in forest vehicles would result in a significant reduction in greenhouse gas emissions and possibly lower maintenance costs.

Machine learning toward advanced energy storage devices and systems

Technology advancement demands energy storage devices (ESD) and systems (ESS) with better performance, longer life, higher reliability, and smarter management strategy. Designing such systems involve a trade-off among a large set of parameters, whereas advanced control strategies need to rely on the instantaneous status of many indicators. Machine learning can dramatically accelerate calculations, capture complex mechanisms to improve the prediction accuracy, and make optimized decisions based on comprehensive status information. The computational efficiency makes it applicable for real-time management. This paper reviews recent progresses in this emerging area, especially new concepts, approaches, and applications of machine learning technologies for commonly used energy storage devices (including batteries, capacitors/supercapacitors, fuel cells, other ESDs) and systems (including battery ESS, hybrid ESS, grid and microgrid-containing energy storage units, pumped-storage system, thermal ESS). The perspective on future directions is also discussed.

Correlation-driven machine learning for accelerated reliability assessment of solder joints in electronics

The quantity and variety of parameters involved in the failure evolutions in solder joints under a thermo-mechanical process directs the reliability assessment of electronic devices to be frustratingly slow and expensive. To tackle this challenge, we develop a novel machine learning framework for reliability assessment of solder joints in electronic systems; we propose a correlation-driven neural network model that predicts the useful lifetime based on the materials properties, device configuration, and thermal cycling variations. The results indicate a high accuracy of the prediction model in the shortest possible time. A case study will evaluate the role of solder material and the joint thickness on the reliability of electronic devices; we will illustrate that the thermal cycling variations strongly determine the type of damage evolution, i.e., the creep or fatigue, during the operation. We will also demonstrate how an optimal selection of the solder thickness balances the damage types and considerably improves the useful lifetime. The established framework will set the stage for further exploration of electronic materials processing and offer a potential roadmap for new developments of such materials.

Studies on Dynamic Properties of Ultracapacitors Using Infinite r–C Chain Equivalent Circuit and Reverse Fourier Transform

The specific power storage capabilities of double-layer ultracapacitors are receiving significant attention from engineers and scientific researchers. Nevertheless, their dynamic behavior should be studied to improve the performance and for efficient applications in electrical devices. This article presents an infinite resistor–capacitor (r–C) chain-based mathematical model for the analysis of double layer ultracapacitors. The internal resistance and capacitance were measured for repetitive charging and discharging cycles. The magnitudes of internal resistance and capacitance showed approximately ±10% changes for charge-discharge processes. Electrochemical impedance spectroscopy investigations revealed that the impedance of a double-layer ultracapacitor does not change significantly in the temperature range of (−30 °C to +30 °C) and voltage range of (0.3376–2.736 V). The analysis of impedance data using the proposed mathematical model showed good agreement between the experimental and theoretical data. The dynamic behavior of the ultracapacitor was successfully represented by utilizing the proposed infinite r–C chains equivalent circuit, and the reverse Fourier transform analysis. The r–C electrical equivalent circuit was also analyzed using the PSIM simulation software to study the dynamic behavior of ultracapacitor parameters. The simulation study yields an excellent agreement between the experimental and calculated voltage characteristics for repetitive charging-discharging processes.

Comprehensive Study on Dynamic Parameters of Symmetric and Asymmetric Ultracapacitors

Electrical storage components such as ultracapacitors (UC) have received significant attention from various industrial sectors, from electric vehicles to renewable power plants. This article presents the investigations on dynamic properties of asymmetric Li-ion hybrid (CPQ2300S: 2300 F, 2.2–3.8 V, JSR Co., Tokyo, Japan) and symmetric double-layer (BCAP3400: 3400 F, 2.85 V, Maxwell Technologies Co., San Diego, CA, USA) ultracapacitors. The internal resistance and capacitance of both UCs were slightly changed with respect to current and voltage alterations, but these changes were more prominent for the Li-ion UC. The internal resistance of the Li-ion UC became five times larger and its capacitance decreased significantly when the temperature decreased from +25 °C to −20 °C. More importantly, the double-layer UC exhibited nearly constant capacitance for a wide range of temperature changes (0 °C to −40 °C), although internal resistance increased somewhat. Electrochemical impedance spectroscopy analysis of both UCs was performed for the frequency range of 1 Hz–1 kHz and in the temperature range from −15 °C to +30 °C. It was observed that the temperature effects were much more pronounced for the asymmetric Li-ion UC than that of the symmetric double-layer UC. This work also proposes an improved equivalent circuit model based on an infinite number of resistance-capacitance (r–C) chains. The characteristic behavior of symmetric UCs can be explained precisely by the proposed model. This model is also applicable to asymmetric UCs, but with less precision.