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Methods for lithium-based battery energy storage SOC estimation. Part I: Overview

Hallmann Marcel, Wenge Christoph, Komarnicki Przemysław, Balischewski Stephan

Archives of Electrical Engineering

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2022

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Vol. 71, nr 1

139--157

EN

The use of lithium-ion battery energy storage (BES) has grown rapidly during the past year for both mobile and stationary applications. For mobile applications, BES units are used in the range of 10–120 kWh. Power grid applications of BES are characterized by much higher capacities (range of MWh) and this area particularly has great potential regarding the expected energy system transition in the next years. The optimal operation of BES by an energy storage management system is usually predictive and based strongly on the knowledge about the state of charge (SOC) of the battery. The SOC depends on many factors (e.g. material, electrical and thermal state of the battery), so that an accurate assessment of the battery SOC is complex. The SOC intermediate prediction methods are based on the battery models. The modeling of BES is divided into three types: fundamental (based on material issues), electrical equivalent circuit (based on electrical modeling) and balancing (based on a reservoir model). Each of these models requires parameterization based on measurements of input/output parameters. These models are used for SOC modelbased calculation and in battery system simulation for optimal battery sizing and planning. Empirical SOC assessment methods currently remain the most popular because they allow practical application, but the accuracy of the assessment, which is the key factor for optimal operation, must also be strongly considered. This scientific contribution is divided into two papers. Paper part I will present a holistic overview of the main methods of SOC assessment. Physical measurement methods, battery modeling and the methodology of using the model as a digital twin of a battery are addressed and discussed. Furthermore, adaptive methods and methods of artificial intelligence, which are important for the SOC calculation, are presented. In paper part II, examples of the application areas are presented and their accuracy is discussed

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Modele akumulatorów wykorzystywane do oceny ich stanu

Jakubiec B.

TTS Technika Transportu Szynowego

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2013

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R. 20, nr 10

3385--3394

PL

W artykule zaprezentowano problematykę modelowania akumulatorów. Scharakteryzowano właściwości akumulatorów używanych zwłaszcza do zasilania układów małej mocy. Przedstawiono modele wykorzystywane w identyfikacji stanu naładowania akumulatorów, jak i szacowania ich zużycia. Opisano rozwiązania najczęściej stosowane i prezentowane w literaturze.

EN

This paper presents a problem of modeling the batteries. The basic technical parameters of the batteries are specified. Battery models proposed in the literature to estimate the battery state of charge and state of health are described.

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Design and Implementation of a UKF-based SOC Estimator for LiMnO2 Batteries Used on Electric Vehicles

Dai H., Wei X., Sun Z.

Przegląd Elektrotechniczny

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2012

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R. 88, nr 1b

57-63

EN

To ensure safe and reliable battery operations, an accurate battery state of charge (SOC) estimation is critical for the battery systems used in electric vehicles and hybrid electric vehicles because of the arduous operation conditions. This paper presents a SOC estimator designed based on the unscented Kalman filter (UKF), which is very popular in the state estimation in non-linear systems. The dynamic characteristics of the battery are modeled with an equivalent circuit, which is composed of two capacitors, three resistors and a voltage source to simulate the equilibrium open circuit voltage (OCV). To relieve the computation requirement of the original UKF, an efficient implementation using a Cholesky factorization is investigated, and thereby a SR-UKF based SOC estimator is proposed. Experiment results shows that the model proposed can track the dynamic behavior of the battery very well and the UKF-based SOC estimator has a good performance in the state estimation, and a comparison with EKFbased estimator also shows that a better accuracy can be got by the proposed UKF- based estimator.

PL

Artykuł prezentuje system kontroli SOC (state of charge – stan naładowania) baterii używanych w pojazdach elektrycznych. System bazuje na filtrze Kalmana typu UKF. Własności dynamiczne baterii modelowane są przy pomocy odpowiedniego obwodu elektrycznego zastępczego. System może śledzić właściwości dynamiczne baterii i badać jej stan naładowania.