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Estimation of the State-of-Charge and State-of-Health of Lithium-Ion Batteries

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D1: Advanced Energy Materials".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 23413

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Special Issue Editor

Dr. Domenico Di Domenico

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Guest Editor

IFP Energies Nouvelles, Rond-point de l’échangeur de Solaize, BP 3, 69360 Solaize, France
Interests: control systems engineering; battery management systems; wind energy; automotive engineering; state estimation; optimal control; data-driven control

Special Issue Information

Dear Colleagues,

I would like to invite you to contribute to a Special Issue of Energies on "Estimation of the State-of-Charge and State-of-Health of Lithium-Ion Batteries".

Lithium-ion batteries are unanimously considered to be the standard electric power source for many applications, including electrified vehicles, such as plug-in hybrid electric vehicles (PHEV), electric vehicles (EV), and hybrid electric vehicles (HEV). Accordingly, optimal battery management is a relevant issue in automatic control practice. The Battery Management System (BMS) has to: (1) ensure that the battery is appropriately used when meeting the demand for electrical power; and (2) guarantee feasible and safe operation.

A battery’s energy and power strongly depend on the battery’s state. In particular, the residual energy is related to the cell’s State-of-Charge (SoC) as well as to the battery’s age, and is the reason why the performance of lithium-ion batteries degrades with time and use. As the direct measurement of SoC and State-of-Health (SoH) is unavailable in real-time applications, a key task for the BMS is to provide an accurate estimation of the battery’s state. This is required in order to achieve high battery efficiency, avoid damage, predict the Remaining Useful Life (RUL), and potentially slow the rate of deterioration.

This Special Issue will focus on the analysis, design, implementation, and validation of strategies for the estimation of the state of lithium-ion cells and, more generally, algorithms for the management of lithium-ion batteries. Studies that employ recently developed data-driven approaches will also be welcome.

Dr. Domenico Di Domenico
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

battery management system
lithium-ion batteries
state estimation
battery aging
state-of-charge estimation
state-of-health estimation
remaining useful life prediction
data-driven estimation

Published Papers (5 papers)

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Research

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12 pages, 1070 KiB

Open AccessArticle

Lithium-Ion Battery Parameter Identification via Extremum Seeking Considering Aging and Degradation

by Iván Sanz-Gorrachategui, Pablo Pastor-Flores, Antonio Bono-Nuez, Cora Ferrer-Sánchez, Alejandro Guillén-Asensio and Carlos Bernal-Ruiz

Energies 2021, 14(22), 7496; https://doi.org/10.3390/en14227496 - 10 Nov 2021

Viewed by 1459

Abstract

Battery parameters such as State of Charge (SoC) and State of Health (SoH) are key to modern applications; thus, there is interest in developing robust algorithms for estimating them. Most of the techniques explored to this end rely on a battery model. As batteries age, their behavior starts differing from the models, so it is vital to update such models in order to be able to track battery behavior after some time in application. This paper presents a method for performing online battery parameter tracking by using the Extremum Seeking (ES) algorithm. This algorithm fits voltage waveforms by tuning the internal parameters of an estimation model and comparing the voltage output with the real battery. The goal is to estimate the electrical parameters of the battery model and to be able to obtain them even as batteries age, when the model behaves different than the cell. To this end, a simple battery model capable of capturing degradation and different tests have been proposed to replicate real application scenarios, and the performance of the ES algorithm in such scenarios has been measured. The results are positive, obtaining converging estimations both with new and aged batteries, with accurate outputs for the intended purpose. Full article

(This article belongs to the Special Issue Estimation of the State-of-Charge and State-of-Health of Lithium-Ion Batteries)

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16 pages, 30959 KiB

Open AccessArticle

Lithium Battery SOH Monitoring and an SOC Estimation Algorithm Based on the SOH Result

by Jong-Hyun Lee and In-Soo Lee

Energies 2021, 14(15), 4506; https://doi.org/10.3390/en14154506 - 26 Jul 2021

Cited by 28 | Viewed by 4020

Abstract

Lithium batteries are the most common energy storage devices in items such as electric vehicles, portable devices, and energy storage systems. However, if lithium batteries are not continuously monitored, their performance could degrade, their lifetime become shortened, or severe damage or explosion could be induced. To prevent such accidents, we propose a lithium battery state of health monitoring method and state of charge estimation algorithm based on the state of health results. The proposed method uses four neural network models. A neural network model was used for the state of health diagnosis using a multilayer neural network model. The other three neural network models were configured as neural network model banks, and the state of charge was estimated using a multilayer neural network or long short-term memory. The three neural network model banks were defined as normal, caution, and fault neural network models. Experimental results showed that the proposed method using the long short-term memory model based on the state of health diagnosis results outperformed the counterpart methods. Full article

(This article belongs to the Special Issue Estimation of the State-of-Charge and State-of-Health of Lithium-Ion Batteries)

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21 pages, 9247 KiB

Open AccessArticle

The Estimation Life Cycle of Lithium-Ion Battery Based on Deep Learning Network and Genetic Algorithm

by Shih-Wei Tan, Sheng-Wei Huang, Yi-Zeng Hsieh and Shih-Syun Lin

Energies 2021, 14(15), 4423; https://doi.org/10.3390/en14154423 - 22 Jul 2021

Cited by 7 | Viewed by 1989

Abstract

This study uses deep learning to model the discharge characteristic curve of the lithium-ion battery. The battery measurement instrument was used to charge and discharge the battery to establish the discharge characteristic curve. The parameter method tries to find the discharge characteristic curve and was improved by MLP (multilayer perceptron), RNN (recurrent neural network), LSTM (long short-term memory), and GRU (gated recurrent unit). The results obtained by these methods were graphs. We used genetic algorithm (GA) to obtain the parameters of the discharge characteristic curve equation. Full article

(This article belongs to the Special Issue Estimation of the State-of-Charge and State-of-Health of Lithium-Ion Batteries)

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17 pages, 4006 KiB

Open AccessArticle

Implementing an Extended Kalman Filter for SoC Estimation of a Li-Ion Battery with Hysteresis: A Step-by-Step Guide

by Benedikt Rzepka, Simon Bischof and Thomas Blank

Energies 2021, 14(13), 3733; https://doi.org/10.3390/en14133733 - 22 Jun 2021

Cited by 49 | Viewed by 7661

Abstract

The growing share of renewable energies in power production and the rise of the market share of battery electric vehicles increase the demand for battery technologies. In both fields, a predictable operation requires knowledge of the internal battery state, especially its state of charge (SoC). Since a direct measurement of the SoC is not possible, Kalman filter-based estimation methods are widely used. In this work, a step-by-step guide for the implementation and tuning of an extended Kalman filter (EKF) is presented. The structured approach of this paper reduces efforts compared with empirical filter tuning and can be adapted to various battery models, systems, and cell types. This work can act as a tutorial describing all steps to get a working SoC estimator based on an extended Kalman filter. Full article

(This article belongs to the Special Issue Estimation of the State-of-Charge and State-of-Health of Lithium-Ion Batteries)

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Review

Jump to: Research

25 pages, 6521 KiB

Open AccessReview

Inhomogeneities and Cell-to-Cell Variations in Lithium-Ion Batteries, a Review

by David Beck, Philipp Dechent, Mark Junker, Dirk Uwe Sauer and Matthieu Dubarry

Energies 2021, 14(11), 3276; https://doi.org/10.3390/en14113276 - 03 Jun 2021

Cited by 51 | Viewed by 7002

Abstract

Battery degradation is a fundamental concern in battery research, with the biggest challenge being to maintain performance and safety upon usage. From the microstructure of the materials to the design of the cell connectors in modules and their assembly in packs, it is impossible to achieve perfect reproducibility. Small manufacturing or environmental variations will compound big repercussions on pack performance and reliability. This review covers the origins of cell-to-cell variations and inhomogeneities on a multiscale level, their impact on electrochemical performance, as well as their characterization and tracking methods, ranging from the use of large-scale equipment to in operando studies. Full article

(This article belongs to the Special Issue Estimation of the State-of-Charge and State-of-Health of Lithium-Ion Batteries)

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