Rishi Relan

Recursive Discrete-Time Models for Continuous-Time Systems Under Band-Limited Assumptions

Discrete-time models are very convenient to simulate a nonlinear system on a computer. In order to build the discrete-time simulation models for the nonlinear feedback systems (which is a very important class of systems in many applications) described as y(t) = g1(u(t), y(t)), one has to solve at each time step a nonlinear algebraic loop for y(t). If a delay is present in the loop, i.e., y(t) = g2(u(t), y(t -1)), fast recursive simulation models can be developed and the need to solve the nonlinear differential-algebraic equations is removed. In this paper, we use the latter to model the nonlinear feedback system using recursive discrete-time models. Theoretical error bounds for such kind of approximated models are provided in the case of band-limited signals, and furthermore, a measurement methodology is proposed for quantifying and validating the output error bounds experimentally.

Data-Driven Nonlinear Identification of Li-Ion Battery Based on a Frequency Domain Nonparametric Analysis

Lithium ion batteries are attracting significant and growing interest, because their high energy and high power density render them an excellent option for energy storage, particularly in hybrid and electric vehicles. In this brief, a data-driven polynomial nonlinear state-space model is proposed for the operating points at the cusp of linear and nonlinear regimes of the battery’s electrical operation, based on the thorough nonparametric frequency domain characterization and quantification of the battery’s behavior in terms of its linear and nonlinear behavior at different levels of the state of charge.

Nonparametric analysis of the short-term electrical response of Li-ion battery cells

In order to develop a complete dynamic model of a lithium ion (Li-ion) batterys electrical behaviour, which is suitable for virtual-prototyping of battery-powered systems, accurate estimation of the state of charge (SoC) and state of health (SoH) is required. This in-turn depends on the quality of the models which are used for the estimation of these quantities. Hence, even before proceeding towards the modelling step, it is important to fully characterize and understand the electrical behaviour of the battery over its full operating range, so that a flexible and accurate dynamic model can be developed. In this paper, a novel frequency domain non-parametric methodology is proposed to characterize the battery short-term electrical behaviour, in terms of: Presence of the non-linearities as well as the time-variations (non-stationary behaviour) over its full operating range. This information can later be used by battery modeller to decide on the modelling methodology.

Output error bounds for discrete-time models with forced delay under band-limited assumptions: An experimental study

Discrete-time models are very convenient to simulate a nonlinear system on a computer. In order to build the fast recursive discrete-time simulation models for the nonlinear feedback systems, one step delay can be introduced to avoid solving at each time step a nonlinear algebraic loop. In this paper, a measurement methodology is proposed to quantify the output error of such class of models. An experimental study based on the proposed measurement methodology is performed in order to validate qualitatively the theoretical error bounds for such kind of approximated linear discrete-time models with forced delay under band-limited assumptions.

Estimation of a stochastic spatio-temporal model of the flow-front dynamics with varying parameters

For control and monitoring purposes, knowledge of the current state of the flow-front in a vacuum assisted resin transfer moulding (VARTM) process is essential. The permeability of the medium and viscosity of the epoxy can change during the infusion process. Especially for online monitoring of the infusion process there is a need for a fast and fairly accurate, possibly virtual sensor system which can handle such parameter variations. Stochastic-differential equations (SDEs) based estimation of the flow-front dynamics can offer a good trade-off between physics and data-driven estimators. In this paper, we analyze the effect of parameter variations on an SDE based spatio-temporal estimator of the flow-front dynamics in a VARTM process.

Data driven discrete-time parsimonious identification of a nonlinear state-space model for a weakly nonlinear system with short data record☆

Abstract Many real world systems exhibit a quasi linear or weakly nonlinear behavior during normal operation, and a hard saturation effect for high peaks of the input signal. In this paper, a methodology to identify a parsimonious discrete-time nonlinear state space model (NLSS) for the nonlinear dynamical system with relatively short data record is proposed. The capability of the NLSS model structure is demonstrated by introducing two different initialisation schemes, one of them using multivariate polynomials. In addition, a method using first-order information of the multivariate polynomials and tensor decomposition is employed to obtain the parsimonious decoupled representation of the set of multivariate real polynomials estimated during the identification of NLSS model. Finally, the experimental verification of the model structure is done on the cascaded water-benchmark identification problem.

A Local Polynomial Approach to Nonparametric Estimation of the Best Linear Approximation of Lithium-Ion Battery From Multiple Datasets

Battery short-term electrical impedance behavior varies between linear, linear time-varying, or nonlinear at different operating conditions. Data-based electrical impedance modeling techniques often model the battery as a linear time-invariant system at all operating conditions. In addition, these techniques require extensive and time consuming experimentation. Often due to sensor failures during experiments, constraints in data acquisition hardware, varying operating conditions, and the slow dynamics of the battery, it is not always possible to acquire data in a single experiment. Hence, multiple experiments must be performed. In this letter, a local polynomial approach is proposed to estimate nonparametrically the best linear approximation of the electrical impedance affected by varying levels of nonlinear distortion, from a series of input current and output voltage data subrecords of arbitrary length.