Circuit Synthesis of Electrochemical Supercapacitor Models

This paper is concerned with the synthesis of RC electrical circuits from physics-based supercapacitor models describing conservation and diffusion relationships. The proposed synthesis procedure uses model discretisation, linearisation, balanced model order reduction and passive network synthesis to form the circuits. Circuits with different topologies are synthesized from several physical models. This work will give greater understanding to the physical interpretation of electrical circuits and will enable the development of more generalised circuits, since the synthesized impedance functions are generated by considering the physics, not from experimental fitting which may ignore certain dynamics

A Modified Exponential Equivalent Parallel Resistance (EPR) Model for Predicting Self-Discharge Behavior of Printed Flexible Supercapacitors

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.acceptedVersionPeer reviewe

Supercapacitor Electro-Mathematical And Machine Learning Modelling For Low Power Applications

Low power electronic systems, whenever feasible, use supercapacitors to store energy instead of batteries due to their fast charging capability, low maintenance and low environmental footprint. To decide if supercapacitors are feasible requires characterising their behaviour and performance for the load profiles and conditions of the target. Traditional supercapacitor models are electromechanical, require complex equations and knowledge of the physics and chemical processes involved. Models based on equivalent circuits and mathematical equations are less complex and could provide enough accuracy. The present work uses the latter techniques to characterize supercapacitors. The data required to parametrize the mathematical model is obtained through tests that provide the capacitors charge and discharge profiles under different conditions. The parameters identified are life cycle, voltage, time, temperature, moisture, Equivalent Series Resistance (ESR) and leakage resistance. The accuracy of this electro-mathematical model is improved with a remodelling based on artificial neuronal networks. The experimental data and the results obtained with both models are compared to verify and weigh their accuracy. Results show that the models presented determine the behaviour of supercapacitors with similar accuracy and less complexity than electromechanical ones, thus, helping scaling low power systems for given conditions

A SuperCapacitor Agent for Providing Real-Time Power Services to the Grid

Supercapacitors-based storage systems are expected to play a key role in microgrids in view of their capability to compensate high-power imbalances. We define an agent for the control of supercapacitor arrays within the context of the novel control framework Commelec, proposed by the Authors as a composable method for real-time control of active distribution networks with explicit power setpoints. An important function of such an agent is to advertise the real-time power capabilities and operational preferences of the supercapacitor array based on local information. Given the small energy capacity of such a device, its internal state can largely vary from one setpoint implementation to the next one. For this reason, the use of an accurate model is crucial in the agent definition. We show that it is possible to infer the real-time power capabilities of the device by using simple measurements on the supercapacitor array suitably coupled with an accurate representation of the cells composing the array. Results show that the agent is able to speak for the resource, thus allowing its use from an external controlle

Enhanced electrical model of Lithium-based batteries accounting the charge redistribution effect

Within the context of the electrical circuit modeling of batteries, this paper proposes an improvement of the most common electric equivalent circuit used for Lithium cells. The main improvement is based on the modeling of the so-called charge redistribution phenomenon that characterizes the dynamic voltage during charging/discharging and relaxation phases. In particular, the aim of the paper is to prove that the model recently proposed by the Authors to represent the same phenomenon in supercapacitors, can be extended also to Lithium batteries. The proposed model is validated by means of experimental results carried out on a 30 Ah 2.3 V Lithium-Titanate cell with reference to different charge/discharge cycles

Improvement of Dynamic Modeling of Supercapacitor by Residual Charge Effects Estimation

The paper presents a set of experimental investigations related to the dynamic behavior of supercapacitors. The experimentally observed results are then used as inputs for the development of an improved version of one of the most common supercapacitor RC-equivalent circuit models. The key improvement concerns the accurate modeling of the diffusion phenomenon of the supercapacitor residual charge during charging/discharging and rest phases. The experimental procedure needed for evaluating the parameters of the proposed model is also given. The accuracy of the obtained model is, then, experimentally validated for different cycles characterized by different dynamics

Double smart energy harvesting system for self-powered industrial IoT

312 p. 335 p. (confidencial)Future factories would be based on the Industry 4.0 paradigm. IndustrialInternet of Things (IIoT) represent a part of the solution in this field. Asautonomous systems, powering challenges could be solved using energy harvestingtechnology. The present thesis work combines two alternatives of energy input andmanagement on a single architecture. A mini-reactor and an indoor photovoltaiccell as energy harvesters and a double power manager with AC/DC and DC/DCconverters controlled by a low power single controller. Furthermore, theaforementioned energy management is improved with artificial intelligencetechniques, which allows a smart and optimal energy management. Besides, theharvested energy is going to be stored in a low power supercapacitor. The workconcludes with the integration of these solutions making IIoT self-powered devices.IK4 Teknike

Advanced Control of Active Distribution Networks Integrating Dispersed Energy Storage Systems

Due to the increased penetration of Distributed Generations (DGs) in distribution networks, the system control and operation may become quite different from the case of traditional network. Most DGs can only provide intermittent power to the Active Distribution Networks (ADNs) due to the intermittent nature of the resources. Moreover, ADN utilities usually do not own DGs, and have difficulty in controlling directly DGs output powers. The main problem related to the considerable connection of DGs is usually associated to the node voltage quality and line congestion mitigation. Within the above context, the motivating factors for this thesis are supported by the issues related to optimal operation and control of ADNs integrating stochastic and non-stochastic DGs. One of the most promising near-term solution is offered by using distributed Energy Storage Systems (ESSs) which can perform their full role to guarantee a more flexible network. Indeed, the availability of ESSs allows, in principle, to: (i) actively control the power flows into the grid, (ii) indirectly control the voltage profiles along the network feeders and (iii) locally balance the hour/daily and weekly load variations. In this thesis, ESSs are assumed to be the only controllable devices in ADNs. As a result, DGs can be indirectly controlled by means of ESSs. First, this manuscript presents control-oriented model for ESSs. In this respect, the accurate estimation of ESS behavior is utmost important. A generic charge representative model for any ESSs is proposed. Moreover, an improvement of the most common electric equivalent circuit models for the two selected ESSs with different characteristics (namely supercapacitors and batteries) is provided for the development of specific control schemes. They are based on the modeling of redistribution of charges that characterizes the dynamic behaviors of the two devices during long time charging/discharging and relaxation phases. Second, this manuscript presents advanced control/scheduling algorithm for ADNs. The operation and control of ADNs can be achieved either centrally or in a decentralized way. The amount of information to be centrally treated would considerably grow due to the number of generation equipmentâs inserted into the grid and the stochastic operation nature of some of them. This consideration introduces the idea that some ADN operation problems, such as voltage control or line congestion mitigation, can be solved in a distributed manner which would help to relieve the information processing burden and to enhance the system security while preventing unwanted event from propagating through the grid. Therefore, the decentralized schemes are considered subdividing the network into quasi-autonomous areas. To this end, given a set of ESSs optimally located in a balanced and radial ADN, this thesis proposes a network partitioning strategy for the optimal voltage control of ADNs. Thus, the network is decomposed into several areas; each under the control of one ESS which has maximum influence on its corresponding area. Based on this clustering, decentralized scheduling strategies and real-time decentralized control algorithms for the clustered ADNs are proposed. The proposed zonal control capability focuses on voltage control and line congestion management. In both proposed decentralized scheduling and real-time control algorithms the communication among different areas is defined using the concept of Multi-Agent Systems