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Battery Management and State Estimation

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (30 April 2018) | Viewed by 125767

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Special Issue Editor

Assoc. Prof. Maciej Swierczynski

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Guest Editor

Department of Energy Technology, Aalborg University, 9220 Aalborg Ø, Denmark
Interests: energy storage; batteries; battery characterization techniques; battery testing; modeling; lifetime testing; lifetime prediction; state estimation; EV; renewable and residential battery applications
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Special Issue Information

Dear Colleagues,

Together with falling prices and technology improvements, battery systems and especially lithium-ion battery systems are gaining importance and market share in various applications (e.g., EVs, HEVs, PHEVs, renewable, residential, UPS). In parallel to the work aiming at battery technology improvements and cost reduction, there is currently a great deal of research in order to improve battery management system (BMS) and accuracy of battery state estimation.

An effective BMS is compulsory for some battery technologies, e.g., for lithium-ion batteries. Modern BMSs are becoming more complicated systems and they manage battery systems in manifold ways. These include, amongst others, battery monitoring, diagnostics, thermal management, energy management, battery cells balancing, early fault detection, critical data storage, communication, etc.

On the other hand, requirements for BMSs are quickly growing and thus, in many battery applications, accurate knowledge about the current state is required in order to assure safety, reliability, minimum downtime and accurate information about battery run-time. In consequence, BMSs need to be able to provide functionalities like accurate state of charge (SOC) estimation, state of health (SOH) estimation, state of function (SOF) estimation (e.g., power capability), remaining useful life (RUL) estimation, etc.

Therefore, this Special Issue is focused on recent progress and developments in battery management and state estimation.

Potential topics include, but are not limited to:

New architectures and progress in battery management systems;
New methods of SOC, SOH, SOF, RUL estimation;
Battery diagnosis and prognostic methods;
Lifetime estimation and modeling of battery degradation;
Safety concerns;
Optimal battery control and management;
Battery thermal management;

Assoc. Prof. Maciej Swierczynski
Guest Editor

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Keywords

Battery Management System
Battery failure modes and early fault detection
Testing and modeling
Cell balancing
Lifetime
Diagnostics
Reliability
SOC, SOH, SOF, RUL estimation
Safety

Published Papers (13 papers)

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Research

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21 pages, 5514 KiB

Open AccessArticle

A Novel Health Factor to Predict the Battery’s State-of-Health Using a Support Vector Machine Approach

by Kai Huang, Yong-Fang Guo, Ming-Lang Tseng, Kuo-Jui Wu and Zhi-Gang Li

Appl. Sci. 2018, 8(10), 1803; https://doi.org/10.3390/app8101803 - 02 Oct 2018

Cited by 16 | Viewed by 2953

Abstract

The maximum available capacity is an important indicator for determining the State-of-Health (SOH) of a lithium-ion battery. Upon analyzing the experimental results of the cycle life and open circuit voltage tests, a novel health factor which can be used to characterize the maximum available capacity was proposed to predict the battery’s SOH. The health factor proposed contains the features extracted from the terminal voltage drop during the battery rest. In real applications, obtaining such health factor has the following advantages. The battery only needs to have a rest after it is charged or discharged, it is easy to implement. Charging or discharging a battery to a specific voltage rather than a specific state of charge which is difficult to obtain the accurate value, so the health factor has high accuracy. The health factor is not dependent on the cycle number of the cycle life test of the battery and it is less dependent on charging or discharging current rate, as a result, the working conditions have less effect on the health factor. Further, the paper adopted a support vector machine approach to connect the healthy factor to the maximum available battery capacity of the battery. The experimental results show that the proposed method can predict the SOH of the battery well. Full article

(This article belongs to the Special Issue Battery Management and State Estimation)

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10 pages, 1978 KiB

Open AccessFeature PaperArticle

Evaluation of Present Accelerated Temperature Testing and Modeling of Batteries

by Weiping Diao, Yinjiao Xing, Saurabh Saxena and Michael Pecht

Appl. Sci. 2018, 8(10), 1786; https://doi.org/10.3390/app8101786 - 01 Oct 2018

Cited by 15 | Viewed by 5447

Abstract

Battery manufacturers and device companies often test batteries at high temperature to accelerate the degradation process. The data collected from these accelerated tests are then used to determine battery performance and reliability over specified nominal operating temperatures. In many cases, companies assume an Arrhenius model, or prescribe a decade rule to conduct the data analysis. This paper presents the flaws in accelerated temperature testing of batteries using the Arrhenius model and the decade rule, with the emphasis on lithium-ion batteries. Experimental case studies demonstrate the inaccuracy of the Arrhenius model. Alternative methods based on reliability science are then provided. Full article

(This article belongs to the Special Issue Battery Management and State Estimation)

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21 pages, 3046 KiB

Open AccessArticle

Battery State Estimation for Lead-Acid Batteries under Float Charge Conditions by Impedance: Benchmark of Common Detection Methods

by Julia Badeda, Monika Kwiecien, Dominik Schulte and Dirk Uwe Sauer

Appl. Sci. 2018, 8(8), 1308; https://doi.org/10.3390/app8081308 - 06 Aug 2018

Cited by 25 | Viewed by 4914

Abstract

Impedance or admittance measurements are a common indicator for the condition of lead-acid batteries in field applications such as uninterruptible power supply (UPS) systems. However, several commercially available measurement units use different techniques to measure and interpret the battery impedance. This paper describes common measurement methods and compares their indication for the state of health (SoH) to those of electrochemical impedance spectroscopy (EIS). For this analysis, two strings consisting each of 24 valve-regulated lead-acid (VRLA) batteries with a rated voltage of 12 V and about 7 Ah capacity were kept under standard UPS conditions in float charge for over 560 days. They were monitored continuously with a LEM Sentinel 2 and went into regular check-ups with impedance measurements by a Hioki BT3554 as well as electrochemical impedance spectroscopy (EIS) measurements with an impedance meter (μEIS). Today it is widely expected that solely the relative increase of the impedance reading is sufficient for the estimation of the available capacity. However, it can be shown that the measured relative increase deviates for different frequencies and therefore the choice of the excitation signal and measurement frequency does make a difference for the calculation of the available capacity. Finally, a method for a more decisive monitoring in field applications is suggested. Full article

(This article belongs to the Special Issue Battery Management and State Estimation)

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12 pages, 4583 KiB

Open AccessArticle

An Adaptive Rapid Charging Method for Lithium-Ion Batteries with Compensating Cell Degradation Behavior

by Dong-Rak Kim, Jin-Wook Kang, Tae-Ho Eom, Jun-Mo Kim, Jeong Lee and Chung-Yuen Won

Appl. Sci. 2018, 8(8), 1251; https://doi.org/10.3390/app8081251 - 29 Jul 2018

Cited by 15 | Viewed by 4720

Abstract

Recent developments in high-density lithium-ion battery technologies have greatly expanded the electric vehicle (EV) market. Due to the fact that the rapid charging of an EV battery pack while maintaining a suitable cell cycle life is necessary for further growth of the EV market, we herein propose an innovative adaptive rapid charging pattern that minimizes cell degradation and reflects the degradation characteristics. This technology is advantageous in that cells can be developed by analyzing the charging characteristics in the latter stages of cell development of the rapid charging pattern, while also considering the complexity and heterogeneity of the manufacturing process. Furthermore, the battery charging pattern is optimized and controlled in real-time by reflecting the characteristics of the battery module and pack degradation as the cycle number is increased. More specifically, we present a preliminary study that simplifies the implementation of the new optimization pattern to improve the cell cycle life by over 45% in comparison to conventional fast charging patterns, and to address the drop in capacity in the latter half of cell life during rapid charging. Full article

(This article belongs to the Special Issue Battery Management and State Estimation)

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24 pages, 1605 KiB

Open AccessArticle

Variation of Impedance in Lead-Acid Batteries in the Presence of Acid Stratification

by Monika Kwiecien, Moritz Huck, Julia Badeda, Caner Zorer, Kuebra Komut, Qianru Yu and Dirk Uwe Sauer

Appl. Sci. 2018, 8(7), 1018; https://doi.org/10.3390/app8071018 - 22 Jun 2018

Cited by 6 | Viewed by 4100

Abstract

Acid stratification is a common issue in lead-acid batteries. The density of the electrolyte rises from the top to the bottom and causes inhomogeneous current distribution over the electrodes. The consequences are unequal aging processes provoking earlier battery failure. In stationary applications electrolyte circulation pumps are sporadical installed in the battery to mix the acid. For automotive applications passive mixing systems are implemented by some battery manufacturers against stratification. Stratification does not show any distinct voltage-current profile to be recognizable online. However, it increases the voltage and affects the impedance, which both are essential information for diagnostic purpose. Impedance spectra were performed here on lead-acid test cells with adjusted stratification levels to analyze the influence on the impedance in details. It is observed, that the high-frequency impedance is decreased in the stratified cell and that in contrast to this the charge-transfer resistance is increased. Based on simulations with a spatially-resolved equivalent electrical circuit the increased charge-transfer resistance could be explained with an inhomogeneous State-of-Charge resulting in an accumulation of sulfate crystals in the bottom part of the electrodes. These sulfate crystals further affected recorded impedance spectra after the electrolyte was homogenized. Full article

(This article belongs to the Special Issue Battery Management and State Estimation)

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16 pages, 7983 KiB

Open AccessArticle

Using Small Capacity Fuel Cells Onboard Drones for Battery Cooling: An Experimental Study

by Shayok Mukhopadhyay, Sheehan Fernandes, Mohammad Shihab and Danial Waleed

Appl. Sci. 2018, 8(6), 942; https://doi.org/10.3390/app8060942 - 06 Jun 2018

Cited by 19 | Viewed by 7082

Abstract

Recently, quadrotor-based drones have attracted a lot of attention because of their versatility, which makes them an ideal medium for a variety of applications, e.g., personal photography, surveillance, and the delivery of lightweight packages. The flight duration of a drone is limited by its battery capacity. Increasing the payload capacity of a drone requires more current to be supplied by the battery onboard a drone. Elevated currents through a Li-ion battery can increase the battery temperature, thus posing a significant risk of fire or explosion. Li-ion batteries are suited for drone applications, due to their high energy density. There have been attempts to use hydrogen fuel cells onboard drones. Fuel cell stacks and fuel tank assemblies can have a high energy to weight ratio. So, they may be able to power long duration drone flights, but such fuel cell stacks and associated systems, are usually extremely expensive. Hence, this work proposes the novel use of a less expensive, low capacity, metal hydride fuel stick-powered fuel cell stack as an auxiliary power supply onboard a drone. A primary advantage of this is that the fuel sticks can be used to cool the batteries, and a side effect is that this slightly reduces the burden on the onboard Li-ion battery and provides a small increment in flight time. This work presents the results of an experimental study which shows the primary effect (i.e., decrease in battery temperature) and the secondary side effect (i.e., a small increment in flight time) obtained by using a fuel cell stack. In this work, a metal hydride fuel stick powered hydrogen fuel cell is used along with a Li-ion battery onboard a drone. Full article

(This article belongs to the Special Issue Battery Management and State Estimation)

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13 pages, 3582 KiB

Open AccessArticle

Online State of Health Estimation for Lithium-Ion Batteries Based on Support Vector Machine

by Zheng Chen, Mengmeng Sun, Xing Shu, Renxin Xiao and Jiangwei Shen

Appl. Sci. 2018, 8(6), 925; https://doi.org/10.3390/app8060925 - 04 Jun 2018

Cited by 80 | Viewed by 5307

Abstract

In this paper, a novel state of health (SOH) estimation method based on partial charge voltage and current data is proposed. The extraction of feature variables, which are energy signal, the Ah-throughput, and the charge duration, is discussed and analyzed. The support vector machine (SVM) with radial basis function (RBF) as kernel function is applied for the SOH estimation. The predictive performance of the SOH by the SVM are performed with full and partial charging data. Experiment results show that the addressed approach enables estimating the SOH accurately for practical application. Full article

(This article belongs to the Special Issue Battery Management and State Estimation)

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23 pages, 5854 KiB

Open AccessFeature PaperArticle

Determination of SoH of Lead-Acid Batteries by Electrochemical Impedance Spectroscopy

by Monika Kwiecien, Julia Badeda, Moritz Huck, Kuebra Komut, Dilek Duman and Dirk Uwe Sauer

Appl. Sci. 2018, 8(6), 873; https://doi.org/10.3390/app8060873 - 25 May 2018

Cited by 53 | Viewed by 7560

Abstract

The aging mechanisms of lead-acid batteries change the electrochemical characteristics. For example, sulfation influences the active surface area, and corrosion increases the resistance. Therefore, it is expected that the state of health (SoH) can be reflected through differentiable changes in the impedance of a lead-acid battery. However, for lead-acid batteries, no reliable SoH algorithm is available based on single impedance values or the spectrum. Additionally, the characteristic changes of the spectrum during aging are unknown. In this work, lead-acid test cells were aged under specific cycle regimes known as AK3.4, and periodic electrochemical impedance spectroscopy (EIS) measurements and capacity tests were conducted. It was examined that single impedance values increased linearly with capacity decay, but with varying slopes depending on the pre-history of the cell and measurement frequency of impedance. Thereby, possible reasons for ineffective SoH estimation were found. The spectra were fitted to an equivalent electrical circuit containing, besides other elements, an ohmic and a charge-transfer resistance of the negative electrode. The linear increase of the ohmic resistance and the charge-transfer resistance were characterized for the performed cyclic aging test. Results from chemical analysis confirmed the expected aging process and the correlation between capacity decay and impedance change. Furthermore, the positive influence of charging on the SoH could be detected via EIS. The results presented here show that SoH estimation using EIS can be a viable technique for lead-acid batteries. Full article

(This article belongs to the Special Issue Battery Management and State Estimation)

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14 pages, 2040 KiB

Open AccessArticle

State-of-Health Identification of Lithium-Ion Batteries Based on Nonlinear Frequency Response Analysis: First Steps with Machine Learning

by Nina Harting, René Schenkendorf, Nicolas Wolff and Ulrike Krewer

Appl. Sci. 2018, 8(5), 821; https://doi.org/10.3390/app8050821 - 19 May 2018

Cited by 34 | Viewed by 9883

Abstract

In this study, we show an effective data-driven identification of the State-of-Health of Lithium-ion batteries by Nonlinear Frequency Response Analysis. A degradation model based on support vector regression is derived from highly informative Nonlinear Frequency Response Analysis data sets. First, an ageing test of a Lithium-ion battery at 25 °C is presented and the impact of relevant ageing mechanisms on the nonlinear dynamics of the cells is analysed. A correlation measure is used to identify the most sensitive frequency range for ageing tests. Here, the mid-frequency range from 1 Hz to 100 Hz shows the strongest correlation to Lithium-ion battery degradation. The focus on the mid-frequency range leads to a dramatic reduction in measurement time of up to 92% compared to standard measurement protocols. Next, informative features are extracted and used to parametrise the support vector regression model for the State of Health degradation. The performance of the degradation model is validated with additional cells and validation data sets, respectively. We show that the degradation model accurately predicts the State of Health values. Validation data demonstrate the usefulness of the Nonlinear Frequency Response Analysis as an effective and fast State of Health identification method and as a versatile tool in the diagnosis of ageing of Lithium-ion batteries in general. Full article

(This article belongs to the Special Issue Battery Management and State Estimation)

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27 pages, 18707 KiB

Open AccessArticle

Battery Management System Hardware Concepts: An Overview

by Markus Lelie, Thomas Braun, Marcus Knips, Hannes Nordmann, Florian Ringbeck, Hendrik Zappen and Dirk Uwe Sauer

Appl. Sci. 2018, 8(4), 534; https://doi.org/10.3390/app8040534 - 30 Mar 2018

Cited by 152 | Viewed by 35302

Abstract

This paper focuses on the hardware aspects of battery management systems (BMS) for electric vehicle and stationary applications. The purpose is giving an overview on existing concepts in state-of-the-art systems and enabling the reader to estimate what has to be considered when designing a BMS for a given application. After a short analysis of general requirements, several possible topologies for battery packs and their consequences for the BMS’ complexity are examined. Four battery packs that were taken from commercially available electric vehicles are shown as examples. Later, implementation aspects regarding measurement of needed physical variables (voltage, current, temperature, etc.) are discussed, as well as balancing issues and strategies. Finally, safety considerations and reliability aspects are investigated. Full article

(This article belongs to the Special Issue Battery Management and State Estimation)

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3632 KiB

Open AccessArticle

Enhanced Prognostic Model for Lithium Ion Batteries Based on Particle Filter State Transition Model Modification

by Buddhi Arachchige, Suresh Perinpanayagam and Raul Jaras

Appl. Sci. 2017, 7(11), 1172; https://doi.org/10.3390/app7111172 - 15 Nov 2017

Cited by 19 | Viewed by 4884

Abstract

This paper focuses on predicting the End of Life and End of Discharge of Lithium ion batteries using a battery capacity fade model and a battery discharge model. The proposed framework will be able to estimate the Remaining Useful Life (RUL) and the Remaining charge through capacity fade and discharge models. A particle filter is implemented that estimates the battery’s State of Charge (SOC) and State of Life (SOL) by utilizing the battery’s physical data such as voltage, temperature, and current measurements. The accuracy of the prognostic framework has been improved by enhancing the particle filter state transition model to incorporate different environmental and loading conditions without retuning the model parameters. The effect of capacity fade in the reduction of the EOD (End of Discharge) time with cycling has also been included, integrating both EOL (End of Life) and EOD prediction models in order to get more accuracy in the estimations. Full article

(This article belongs to the Special Issue Battery Management and State Estimation)

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3312 KiB

Open AccessArticle

Comparative Research on RC Equivalent Circuit Models for Lithium-Ion Batteries of Electric Vehicles

by Lijun Zhang, Hui Peng, Zhansheng Ning, Zhongqiang Mu and Changyan Sun

Appl. Sci. 2017, 7(10), 1002; https://doi.org/10.3390/app7101002 - 28 Sep 2017

Cited by 156 | Viewed by 14107

Abstract

Equivalent circuit models are a hot research topic in the field of lithium-ion batteries for electric vehicles, and scholars have proposed a variety of equivalent circuit models, from simple to complex. On one hand, a simple model cannot simulate the dynamic characteristics of batteries; on the other hand, it is difficult to apply a complex model to a real-time system. At present, there are few systematic comparative studies on equivalent circuit models of lithium-ion batteries. The representative first-order resistor-capacitor (RC) model and second-order RC model commonly used in the literature are studied comparatively in this paper. Firstly, the parameters of the two models are identified experimentally; secondly, the simulation model is built in Matlab/Simulink environment, and finally the output precision of these two models is verified by the actual data. The results show that in the constant current condition, the maximum error of the first-order RC model is 1.65% and the maximum error for the second-order RC model is 1.22%. In urban dynamometer driving schedule (UDDS) condition, the maximum error of the first-order RC model is 1.88%, and for the second-order RC model the maximum error is 1.69%. This is of great instructional significance to the application in practical battery management systems for the equivalent circuit model of lithium-ion batteries of electric vehicles. Full article

(This article belongs to the Special Issue Battery Management and State Estimation)

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Review

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17 pages, 6453 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Overview of Lithium-Ion Battery Modeling Methods for State-of-Charge Estimation in Electrical Vehicles

by Jinhao Meng, Guangzhao Luo, Mattia Ricco, Maciej Swierczynski, Daniel-Ioan Stroe and Remus Teodorescu

Appl. Sci. 2018, 8(5), 659; https://doi.org/10.3390/app8050659 - 25 Apr 2018

Cited by 210 | Viewed by 17474

Abstract

As a critical indictor in the Battery Management System (BMS), State of Charge (SOC) is closely related to the reliable and safe operation of lithium-ion (Li-ion) batteries. Model-based methods are an effective solution for accurate and robust SOC estimation, the performance of which heavily relies on the battery model. This paper mainly focuses on battery modeling methods, which have the potential to be used in a model-based SOC estimation structure. Battery modeling methods are classified into four categories on the basis of their theoretical foundations, and their expressions and features are detailed. Furthermore, the four battery modeling methods are compared in terms of their pros and cons. Future research directions are also presented. In addition, after optimizing the parameters of the battery models by a Genetic Algorithm (GA), four typical battery models including a combined model, two RC Equivalent Circuit Model (ECM), a Single Particle Model (SPM), and a Support Vector Machine (SVM) battery model are compared in terms of their accuracy and execution time. Full article

(This article belongs to the Special Issue Battery Management and State Estimation)

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