Battery-powered embedded systems have known a rapid evolution in recent years, as nickel-metal hydride (Ni-MH) battery technology has enabled important reductions in size and proportional increases in total capacity over the older nickel-cadmium (Ni-Cd) and lead-acid battery types. This paper addresses the problem of state-of-health (SoH) estimation and prediction for use in resource-constrained Ni-MH-battery-powered embedded systems. We propose a novel SoH prediction methodology, presenting both a theoretical analysis of the estimation algorithm and the detailed description of hardware and software implementation. Two versions of estimation algorithms are proposed, along with the analysis of their performances in terms of prediction accuracy and required processing power, as the SoH prediction is designed to run online, being part of an embedded battery management system.

Online State-of-Health Assessment for Battery Management Systems

https://isiarticles.com/bundles/Article/pre/pdf/25439.pdf

Microgrid Reliability Modeling and Battery Scheduling Using Stochastic Linear Programming

The evolution of the power grid calls for a smarter infrastructure, capable of integrating different energy sources and all the different kind of users of the grid. In addition, it should be able to dynamically adapt to fluctuations in loads and sources. In the Smart Grid scenario, energy storage systems (ESSs) play a fundamental role, allowing for decoupling production and usage times. Lithium ion batteries are among the most promising technologies in ESSs. They will allow for a full exploitation of renewable energy sources, can be used to shape load curves and constitute the energy reserve in battery electric vehicles. In addition, if such vehicles are plugged into the power grid, they can act as support electricity storage, realizing the so-called vehicle-to-grid (V2G) paradigm. In all these applications, the reliability of the battery system must be guaranteed. Therefore, measurement techniques to assess the state of health of batteries are needed. In this paper, with particular reference to V2G applications, two fast techniques to compute an index representing the state of health of a Li-ion battery are introduced. The first one relies on fuzzy logic, while the second is based on a neural network. In both cases, an early characterization of batteries in the same family of the ones to be monitored is needed. Compared with experimental data, the techniques exhibit good performances without the need for great processing power. Their performances are adequate for the intended V2G fleet management purpose.

Measurement Techniques for Online Battery State of Health Estimation in Vehicle-to-Grid Applications

In this paper, an overview of energy storage systems alternatives to use in medium energy scale applications is done. The considered technologies are compressed air, pumped hydro, superconductors, flywheels and supercapacitors. The last four are suitable for the medium scale applications (as 100 kW photovoltaic generation plants) which this paper is focused on. The supercapacitor alternative is studied in more detail, presenting these device structures, how they can be modeled and their principal applications. The overview is carried out after a detailed reference selection.

/pdf/overview-of-medium-scale-energy-storage-systems-7zxtvauy7p.pdf

Overview of medium scale energy storage systems

The use of supercapacitors as energy storage systems is evaluated in this work. Supercapacitors are compared with other technologies such as compressed air, pumped hydro, superconductors and flywheels. This paper is focused on medium scale energy storage systems (applied to 100 kW photovoltaic generation plants). The supercapacitor is studied in detail, presenting these device structures, how they can be modeled, the balancing, their useful life and their principal applications. The overview is carried out after a detailed reference selection.

http://peandes.unex.es/archives%5CP126.pdf

Supercapacitors: Alternative Energy Storage Systems

A partial differential equation for the lateral motion of a web conveyance system is derived by modeling the web as a viscoelastic beam under axial tension. This model treats the web position between rollers as a function of both time and space, assumes that there is no slip between the web material and the rollers, and incorporates the web material's viscoelastic damping property. A finite-difference approximation of the model is used to simulate a typical two-span web system. The finite-difference approximation is validated by comparing its frequency responses with those of an analytical frequency domain model. The analytical frequency domain model is used to design feedback compensation strategies that make the two-span web system less sensitive to upstream disturbances. The results show that, using a transverse vibration model incorporating viscoelasticity to design even simple classical controllers, it is possible to make the web system less sensitive to upstream disturbances at the sensor location.

Control of lateral motion in moving webs

This paper presents a design technique for integration operators which allows the simulation time step to be chosen independent of the system eigenvalues while guaranteeing stability of the simulation. The technique is particularly useful for the real-time simulation of stiff systems where critical constraints are often imposed on the simulation increment. A significant savings in computation time can be realized with this technique when compared with conventional methods for simulating stiff systems. Examples are presented which illustrate the stability and accuracy properties of this design method.

Integration Operator Design for Real-Time Digital Simulation

A model reduction method based on the least squares algorithm is derived. This method calculates a low order autoregressive moving average (ARMA) predictor equation from a high order ARMA equation. The low order ARMA equation minimizes the sum of the squares of the prediction errors when the input is white noise. This is almost equivalent to minimizing the sum of the squares of the error in the impulse response function. Transfer function models can also be used and a steady-state gain constraint can be incorporated into the procedure. The merits of this method of model order reduction are shown with three examples. In these examples, the proposed method produced results that compared favorably with highly regarded existing model reduction techniques which require many more computations.

Least squares model reduction

This chapter explores the development of deadbeat control and one-step-ahead control Deadbeat control and one-step-ahead control, each have a long history, the two were developed in separate research streams, and it was many years before these streams merged It compares and contrast deadbeat and one-step-ahead control strategies, to contribute to the unification of the two areas, and to clear up some possible points of confusion The chapter discusses that system identification, deadbeat control, and one-step-ahead control are possible because discrete-time systems are described by difference equations, which define an algebraic relation between the system inputs and outputs Such procedures cannot be applied to continuous-time systems, whose inputs and outputs are related by differential equations As a result, for example, deadbeat response in a continuous-time system cannot be achieved via linear time invariant feedback No matter how rapid the step response of a continuous-time control system is made, the exponentially decaying error vanishes only as time goes to infinity

Techniques in Deadbeat and One-step-ahead Control

An explicit linear multistep matrix-integration technique is presented for vector systems of ODEs which employs the stability region placement approach to permit the time-step to be chosen independently of system eigenvalues. Closed-form solutions for the general p-step method and the case where the system matrix has zero eigenvalues are given. It is shown that system mode shapes are preserved over the integration process, and that the technique remains applicable to systems with eigenvalues at their origin without need for computing a matrix inversion.

Tom T. Hartley

Papers

Control of lateral motion in moving webs

Integration Operator Design for Real-Time Digital Simulation

Least squares model reduction

Techniques in Deadbeat and One-step-ahead Control

Multistep matrix integrators for real-time simulation