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

Real-time control strategies for hybrid vehicles issued from optimization algorithm

International audienceThis paper focuses on a mild-hybrid city car (Smart), equipped with a starter-alternator, where the kinetic energy in the braking phases can be recovered to be stored in a supercapacitor, and re-used later via the electric motor. The additional traction power allows to downsize the engine and still fulfill the power requirements. Moreover, the engine can be turned off in idle phases. The optimal control problem of the energy management between the two power sources is solved for given driving cycles by a classical dynamic programming method. From dynamic models of the electric motor and supercapacitor a quasistatic model of the whole system is derived and used in the optimization. The real time control law to be implemented on the vehicle is derived from the resulting optimal control strategies

Investigation of the Effects of Fuel Cells on V-Q & V-P Characteristics

In this paper, the use of a FC system connected to the network is proposed as a source of DG with high reliability, and for this purpose, the dynamic model of the fuel cell has been simulated. A hybrid system of fuel cell distributed generation (FCDG) is presented to provide electrical energy for a small isolated area. Boost converter (DC/DC), in order to increase the voltage level of the system and stabilize the DC link voltage has been used, which provides the possibility of connecting several different scattered production sources in parallel. Voltage stability is concerned with the ability of a power system to maintain acceptable bus voltages under normal conditions and after being subjected to a disturbance. The use of DG sources has many advantages, including meeting peak load needs, reducing network losses, providing reactive power locally, and regulating network voltage. Among all sources of distributed production, fuel cells are of special importance due to their high efficiency, high energy density, the ability to simultaneously produce heat and electric power, and low emission of pollutants. Using fuel cells (FCs) have several advantages and in this paper we investigate the effects of FCs on power systems via simulation a single machine (DG as small gas turbine coupled with a FC)   in the Dig Silent area for different PF of FC. Different PF for FC obtained with control the DC to AC inverter.  We found that by control the PF of FCs, we can increase the limitation of reactive generation of overall system and improve the V-P V-Q characteristics of overall system. With the grid-connected inverter's switching control, the active and reactive power injected into the grid is controlled independently

ENIGMA-A centralised supervisory controller for enhanced onboard electrical energy management with model in the loop demonstration

A centralised smart supervisor (CSS) controller with enhanced electrical energy management (E2-EM) capability has been developed for an Iron Bird Electrical Power Generation and Distribution System (EPGDS) within the Clean Sky 2 ENhanced electrical energy MAnagement (ENIGMA) project. The E2-EM strategy considers the potential for eliminating the 5 min overload capability of the generators to achieve a substantial reduction in the mass of the EPGDS. It ensures optimal power and energy sharing within the EPGDS by interfacing the CSS with the smart grid network (SGN), the energy storage and regeneration system (ESRS), and the programmable load bank 1 secondary distribution board (PLB1 SDU) during power overloads and failure conditions. The CSS has been developed by formalizing E2-EM logic as an algorithm operating in real time and by following safety and reliability rules. The CSS undergoes initial verification using model-in-the-loop (MIL) testing. This paper describes the EPGDS simulated for the MIL testing and details the E2-EM strategy, the algorithms, and logic developed for the ENIGMA CSS design. The CSS was subjected to two test cases using MIL demonstration, and based on the test results, the performance of the ENIGMA CSS is verified and validated

Optimal Power Allocation in Battery/Supercapacitor Electric Vehicles using Convex Optimization

This paper presents a framework for optimizing the power allocation between a battery and supercapacitor in an electric vehicle energy storage system. A convex optimal control formulation is proposed that minimizes total energy consumption whilst enforcing hard constraints on power output and total energy stored in the battery and supercapacitor. An alternating direction method of multipliers (ADMM) algorithm is proposed, for which the computational and memory requirements scale linearly with the length of the prediction horizon (and can be reduced using parallel processing). The optimal controller is compared with a low-pass filter against an all-battery baseline in numerical simulations, where it is shown to provide significant improvement in battery degradation (inferred through reductions of 71.4% in peak battery power, 21.0% in root-mean-squared battery power, and 13.7% in battery throughput), and a reduction of 5.7% in energy consumption. It is also shown that the ADMM algorithm can solve the optimization problem in a fraction of a second for prediction horizons of more than 15 minutes, and is therefore a promising candidate for online receding-horizon control

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

Electrical performance and economic feasibility analyses of hybrid and battery storage devices used in remote area islanded renewable energy systems

South Africa has the fifth largest coal based utility grid in the world, unfortunately many regions in the country are simply too remote for connection with this grid thus have no electricity access [1]. Many remote areas possess high wind speeds and solar irradiance exposure, which makes them ideal for Renewable Energy Systems (RES) but the electrical and economic viability of this deployment, is still in question. Based on these observations, an electrical performance analysis and economic feasibility study based on islanded RES deployment in remote areas of SA is conducted. RES growth is restricted to the effectiveness of its energy management strategy. Pumped Hydro Storage (PHS) is the cheapest islanded large scale storage option but its assignment is restricted to applicable an landscape and terrain [2], [3]. After conducting a critical review, the Lead Acid Battery Storage System (BSS) and Hybrid Battery Supercapacitor Storage (HBS) were over the PHS. A theory development study on established generations systems and storage models was used to compare software designs which resulted in the selection of Matlab software for electric performance analysis and HOMER for the economic feasibility study. The electric performance analysis was divided into three case studies based on the input power supply, viz. ideal voltage source, Solar Photo Voltaic (PV) and Wind Energy Conversion System (WECS), with each case being connected to a BSS and HBS. A load profile and solar and wind resource investigation was conducted using the NASA, Wind Atlas of South Africa (WASA) and Solar GIS database. Electrical cases were modelled in Matlab and evaluated in terms of power security, load matching, power response and charge algorithm accuracy. The results showed that deploying an islanded RES in South Africa is indeed electrically feasible based on the high power security, load matching accuracy, and disturbance response seen in the solar-RES cases. The wind-RES maintained an uninterruptable power supply but failed to match the load as accurately. Cases which used the HBS showed improvements in power stability; load fluctuation response and an extension of storage device lifespan when compared to the BSS connected cases. This was due to the supercapacitor high power density which made it ideal for the compensation of RES and load fluctuations. Three new cases were established for the economic study as follows; solar, wind and hybrid solar-wind generation all tested under BSS and HBS conditions once again. A socio economic study established the region of deployment, natural resources, terrain, landscape as well as the price of WECS, PV, storage, and converter components. These findings were used in HOMER to construct an optimised combination of components required for the supply of a 5MWh/d average load. This was followed by a sensitivity analysis which conducted 14 different optimisations at loads ranging from 1-10MWh/d. Economic benefits of the supercapacitor power density was uncovered through a reduction of the required RES Peak Operating Reserve (POR) capacity. This is especially significant in islanded RES, as they demand large POR in order to maintain autonomous power supply. This amounted to substantial NPC savings ranging from $1 -$7.5 million for the 25 year project. What was more interesting was the hybrid wind-solar generation results of the last case which extended total NPC savings, by up to $10 million. The hybrid-HBS does show some POR reductions which brought the COE to 0.3$/kWh on average, with the hybrid-BSS at 0.35$/kWh. The hybrid-BSS is slightly more expensive but has a reduced complexity which can be more inviting to project engineers therefore both hybrid cases are exceptionally feasible for local RES deployment. Single source RES is indeed electrically and economically feasible and shows extended sizing and performance benefits when implementing HBS. However, the cost reductions and performance benefits of hybrid generation make it the most practical solution to islanded RES in South Africa