Abstract
This paper presents a reduced-order electrochemical battery model designed for the online implementation of battery control systems. The model is based on porous-electrode and concentrated-solution theory frameworks and is able to predict voltage as well as the internal electrochemical variables of a battery. The reduction of the model leads to a physics-based one-dimensional discrete-time state-space reduced-order model (ROM), which is especially beneficial for online systems. Models optimized around different operational setpoints are combined to predict cell variables over a wide range of temperatures and state of charges (SOCs) using the output-blending method. A sigma-point Kalman filter is further used to manage inaccuracies generated by the reduction process and experimental-related issues such as measurement error (noise) in the current and voltage sensors. The state-estimation accuracies are measured against a full-order model (FOM) developed in COMSOL. The whole system is able to track the internal variables of the cell, as well as the cell voltage and SOC with very high accuracy, demonstrating its suitability for an online battery control system.
Highlights
Energy storage systems (ESSs) are a key factor in the energetic transition that is presently taking place in the world
Instead, since this paper presents the first application of sigma-point Kalman filter (SPKF) to a discrete-time realization algorithm (DRA)-produced reduced-order model (ROM) using output blending, we first explore the estimation accuracy by comparing estimates to “true” values produced by a simulation of the full-order model (FOM)
The method produces estimates of cell terminal voltage, and assessing the estimation accuracy of this variable alone can be a good indicator of the overall method, but it is not enough to prove accurate estimates of cell internal electrochemical variables due to the low impact that some of them have on the voltage response
Summary
Energy storage systems (ESSs) are a key factor in the energetic transition that is presently taking place in the world. Smith et al built on this work to derive transfer functions for solid–electrolyte interphase potential difference, solid surface concentration, and lithium flux versus applied cell electrical current [18] They generated a low-order state-space model through a residue-grouping approach [19]. Lee et al derived additional transfer functions for solid potential, electrolyte potential, and electrolyte concentration versus applied cell electrical current and proposed a novel discrete-time realization algorithm (DRA) to produce a reduced-order discretetime state-space model of a lithium-ion battery cell [20], [21].
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