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Advances in Modeling Methods for Battery Life Prediction and Performance Evaluation

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D2: Electrochem: Batteries, Fuel Cells, Capacitors".

Deadline for manuscript submissions: closed (15 March 2023) | Viewed by 25538

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Special Issue Editors

Dr. Md Sazzad Hosen

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Battery Innovation Center (MOBI Research Group), Department of Electrical Engineering and Energy Technology, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Interests: Li-ion battery technologies; cell selection; and battery sizing; cell characterization; battery states estimation (SoC, SoH, SoE, SoP); battery aging; lifetime modeling; algorithm development; thermal management; diagnosis; prognosis of energy storage devices
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Dr. Theodoros Kalogiannis

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Guest Editor

Department of Electrical Engineering and Energy Technology, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Interests: battery characterization; electrical modeling; thermal modeling; battery pack design for PHEV and EV applications; Hardware in the Loop (HiL); V2G; fast charging strategies

Special Issue Information

Dear Colleagues,

The widespread use of batteries, which are the most common energy storage systems in automotive and consumer electronics, have made them an integral part of our daily lives. Crucial concerns, such as battery life, thus require significant attention that is often tackled by modeling. Researchers have made remarkable advancements to develop models that can predict the battery lifetime, state of health (SoH), remaining useful life, etc. outlining the aging behavior. Numerous modeling methodologies from physics-inspired to black-box methods have improved the prediction modeling accuracy by several folds.

This Special Issue highlights research efforts towards advanced battery lifetime prediction methodologies and/or algorithm development studies, in terms of contributions (i.e., research/perspective/review articles). Novel methodologies and characterization techniques to predict battery aging could also be included for battery diagnosis and prognosis from cell to pack level. Authors are encouraged to submit original articles addressing including, but not limited to, the following topics:

Battery aging and lifetime prediction models;
Battery state of health/power estimation;
Remaining useful life prediction;
Rest time based or accelerated aging investigation;
Advanced algorithms for battery life prediction;
Diagnosis and prognosis of battery systems;
Physics-informed aging modeling;
AI or data-driven battery life prediction.

Dr. Md Sazzad Hosen
Dr. Theodoros Kalogiannis
Guest Editors

Manuscript Submission Information

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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

lifetime modeling
aging modeling
state of health estimation
remaining useful life prediction
degradation study
data-driven battery modeling
capacity fade modeling
resistance growth modeling
online estimation
diagnosis and prognosis
realistic validation

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Related Special Issue

Advances in Modeling Methods for Battery Life Prediction and Performance Evaluation (Volume II) in Energies (5 articles)

Published Papers (9 papers)

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14 pages, 738 KiB

Open AccessFeature PaperArticle

Online Lifetime Prediction for Lithium-Ion Batteries with Cycle-by-Cycle Updates, Variance Reduction, and Model Ensembling

by Calum Strange, Rasheed Ibraheem and Gonçalo dos Reis

Energies 2023, 16(7), 3273; https://doi.org/10.3390/en16073273 - 6 Apr 2023

Cited by 6 | Viewed by 3362

Abstract

Lithium-ion batteries have found applications in many parts of our daily lives. Predicting their remaining useful life (RUL) is thus essential for management and prognostics. Most approaches look at early life prediction of RUL in the context of designing charging profiles or optimising cell design. While critical, said approaches are not directly applicable to the regular testing of cells used in applications. This article focuses on a class of models called ‘one-cycle’ models which are suitable for this task and characterized by versatility (in terms of online prediction frameworks and model combinations), prediction from limited input, and cells’ history independence. Our contribution is fourfold. First, we show the wider deployability of the so-called one-cycle model for a different type of battery data, thus confirming its wider scope of use. Second, reflecting on how prediction models can be leveraged within battery management cloud solutions, we propose a universal Exponential-smoothing (e-forgetting) mechanism that leverages cycle-to-cycle prediction updates to reduce prediction variance. Third, we use this new model as a second-life assessment tool by proposing a knee region classifier. Last, using model ensembling, we build a “model of models”. We show that it outperforms each underpinning model (from in-cycle variability, cycle-to-cycle variability, and empirical models). This ‘ensembling’ strategy allows coupling explainable and black-box methods, thus giving the user extra control over the final model. Full article

(This article belongs to the Special Issue Advances in Modeling Methods for Battery Life Prediction and Performance Evaluation)

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14 pages, 8000 KiB

Open AccessArticle

A Post-Mortem Study Case of a Dynamically Aged Commercial NMC Cell

by Md Sazzad Hosen, Poonam Yadav, Joeri Van Mierlo and Maitane Berecibar

Energies 2023, 16(3), 1046; https://doi.org/10.3390/en16031046 - 17 Jan 2023

Cited by 2 | Viewed by 1919

Abstract

Lithium-ion batteries are currently the pioneers of green transition in the transportation sector. The nickel-manganese-cobalt (NMC) technology, in particular, has the largest market share in electric vehicles (EVs), offering high specific energy, optimized power performance, and lifetime. The aging of different lithium-ion battery technologies has been a major research topic in the last decade, either to study the degradation behavior, identify the associated aging mechanisms, or to develop health prediction models. However, the lab-scale standard test protocols are mostly utilized for aging characterization, which was deemed not useful since batteries are supposed to age dynamically in real life, leading to aging heterogeneity. In this research, a commercial NMC variation (4-4-2) was aged with a pragmatic standard-drive profile to study aging behavior. The characterized measurable parameters were statistically investigated before performing an autopsy on the aged battery. Harvested samples of negative and positive electrodes were analyzed with Scanning Electron Microscopy (SEM) and the localized volumetric percentile of active materials was reported. Loss of lithium inventory was found to be the main aging mechanism linked to 20% faded capacity due to heavy electrolyte loss. Sparsely distributed fluorine from the lithium salt was found in both electrodes as a result of electrolyte decomposition. Full article

(This article belongs to the Special Issue Advances in Modeling Methods for Battery Life Prediction and Performance Evaluation)

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16 pages, 4984 KiB

Open AccessArticle

Life Evaluation of Battery Energy System for Frequency Regulation Using Wear Density Function

by Jingyeong Park, Jeonghyeon Choi, Hyeondeok Jo, Daisuke Kodaira, Sekyung Han and Moses Amoasi Acquah

Energies 2022, 15(21), 8071; https://doi.org/10.3390/en15218071 - 30 Oct 2022

Cited by 2 | Viewed by 2314

Abstract

Frequency regulation (FR) using a battery energy storage system (BESS) has been expanding because of the growth of renewable energy. This study introduces the wear density function, which considers battery degradation factors such as the rate of current, temperature, and depth of discharge (DOD) to provide a precise lifespan prediction. Furthermore, an equivalent system model is developed to evaluate the FR performance of the BESS for various operating parameters. Finally, a quantitative tradeoff relationship between performance and battery lifecycle is derived from the analysis using operational data of the actual BESS for FR. Full article

(This article belongs to the Special Issue Advances in Modeling Methods for Battery Life Prediction and Performance Evaluation)

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13 pages, 4471 KiB

Open AccessArticle

Modeling the Combined Effects of Cyclable Lithium Loss and Electrolyte Depletion on the Capacity and Power Fades of a Lithium-Ion Battery

by Dongcheul Lee, Byungmook Kim, Chee Burm Shin, Seung-Mi Oh, Jinju Song, Il-Chan Jang and Jung-Je Woo

Energies 2022, 15(19), 7056; https://doi.org/10.3390/en15197056 - 26 Sep 2022

Viewed by 1710

Abstract

In this study, we present a modeling approach to estimate the combined effects of cyclable lithium loss and electrolyte depletion on the capacity and discharge power fades of lithium-ion batteries (LIBs). The LIB cell based on LiNi_0.6Co_0.2Mn_0.2O₂ (NCM622) was used to model the discharge behavior in the multiple degradation modes. The discharge voltages for nine different levels of cyclable lithium loss and electrolyte depletion were measured experimentally. When there was no cyclable lithium loss, the 50% of electrolyte depletion brought about 5% reduction in discharge capacity at 0.05 C discharge rate, while it resulted in 46% reduction when it was coupled with 30% of cyclable lithium loss. The 50% of electrolyte depletion with no cyclable lithium loss caused 1% reduction in discharge power during 0.5 C discharge at the state of charge (SOC) level of 0.8, while it resulted in 13% reduction when it was coupled with 30% of cyclable lithium loss. The modeling results obtained by using the one-dimensional finite element method were compared with the experimental data. The justification of the modeling methods is demonstrated by the high degree of concordance between the predicted and experimental values. Using the validated modeling methodology, the discharge capacity and usable discharge power can be estimated effectively under various combined degradation modes of cyclable lithium loss and electrolyte depletion in the LIB cell. Full article

(This article belongs to the Special Issue Advances in Modeling Methods for Battery Life Prediction and Performance Evaluation)

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15 pages, 5512 KiB

Open AccessArticle

Model Development for State-of-Power Estimation of Large-Capacity Nickel-Manganese-Cobalt Oxide-Based Lithium-Ion Cell Validated Using a Real-Life Profile

by Abraham Alem Kebede, Md Sazzad Hosen, Theodoros Kalogiannis, Henok Ayele Behabtu, Towfik Jemal, Joeri Van Mierlo, Thierry Coosemans and Maitane Berecibar

Energies 2022, 15(18), 6497; https://doi.org/10.3390/en15186497 - 6 Sep 2022

Cited by 1 | Viewed by 1529

Abstract

This paper investigates the model development of the state-of-power (SoP) estimation for a 43 Ah large-capacity prismatic nickel-manganese-cobalt oxide (NMC) based lithium-ion cell with a thorough aging investigation of the cells’ internal resistance increase. For a safe operation of the vehicle system, a battery management system (BMS) integrated with SoP estimation functions is crucial. In this study, the developed SoP model used for the estimation of power throughout the lifetime of the cell is coupled with a dual-polarization equivalent-circuit model (DP_ECM) for achieving the precise estimation of desired parameters. The SoP model is developed based on the pulse-trained internal resistance evolution approach, and hence the power is estimated by determining the rate of internal resistance increase. Hybrid pulse power characterization (HPPC) test results are used for extraction of the impedance parameters. In the DP_ECM, Coulomb counting and extended Kalman filter (EKF) state estimation methods are developed for the accurate estimation of the state of charge (SoC) of the cell. The SoP model validation is performed by using both dynamic Worldwide harmonized Light vehicles Test Cycles (WLTC) and static current profiles, achieving promising results with root-mean-square errors (RMSE) of 2% and 1%, respectively. Full article

(This article belongs to the Special Issue Advances in Modeling Methods for Battery Life Prediction and Performance Evaluation)

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17 pages, 7677 KiB

Open AccessArticle

Prediction of Battery SOH by CNN-BiLSTM Network Fused with Attention Mechanism

by Shuo Sun, Junzhong Sun, Zongliang Wang, Zhiyong Zhou and Wei Cai

Energies 2022, 15(12), 4428; https://doi.org/10.3390/en15124428 - 17 Jun 2022

Cited by 28 | Viewed by 3101

Abstract

During the use and management of lead–acid batteries, it is very important to carry out prediction and study of the state of the health (SOH) of the battery. To this end, this paper proposes a SOH prediction method for lead–acid batteries based on the CNN-BiLSTM-Attention model. The model utilizes the convolutional neural network (CNN) to carry out feature extraction and data dimension reduction in the input factors of model, and then these factors are used as the input of the bidirectional long short-term memory network (BiLSTM). The BiLSTM is used to learn the temporal correlation information in the local features of input time series bidirectionally. The attention mechanism is introduced to assign more attention to key features in the input sequence with more significant influence on the output result by assigning weights to important features, and finally, multi-step prediction of the battery SOH is realized. Compared with the prediction results of battery SOH using other neural network methods, the method proposed in this study can provide higher prediction accuracy and achieve accurate multi-step prediction of battery SOH. Measured results show that most of the multi-step prediction errors of the proposed method are controlled within 3%. Full article

(This article belongs to the Special Issue Advances in Modeling Methods for Battery Life Prediction and Performance Evaluation)

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18 pages, 7071 KiB

Open AccessArticle

Mixed-Integer Linear Programming Model to Assess Lithium-Ion Battery Degradation Cost

by Débora B. S. Oliveira, Luna L. Glória, Rodrigo A. S. Kraemer, Alisson C. Silva, Douglas P. Dias, Alice C. Oliveira, Marcos A. I. Martins, Mathias A. Ludwig, Victor F. Gruner, Lenon Schmitz and Roberto F. Coelho

Energies 2022, 15(9), 3060; https://doi.org/10.3390/en15093060 - 22 Apr 2022

Cited by 4 | Viewed by 2432

Abstract

This work proposes a mixed-integer linear programming model for the operational cost function of lithium-ion batteries that should be applied in a microgrid centralized controller. Such a controller aims to supply loads while optimizing the leveled cost of energy, and for that, the cost function of the battery must compete with the cost functions of other energy resources, such as distribution network, dispatchable generators, and renewable sources. In this paper, in order to consider the battery lifetime degradation, the proposed operational cost model is based on the variation in its state of health (SOH). This variation is determined by experimental data that relate the number of charge and discharge cycles to some of the most important factors that degrade the lifespan of lithium-ion batteries, resulting in a simple empirical model that depends on the battery dispatch power and the current state of charge (SOC). As proof-of-concept, hardware-in-the-loop (HIL) simulations of a real microgrid are performed considering a centralized controller with the proposed battery degradation cost function model. The obtained results demonstrate that the proposed cost model properly maintains the charging/discharging rates and the SOC at adequate levels, avoiding accelerating the battery degradation with use. For the different scenarios analyzed, the battery is only dispatched to avoid excess demand charges and to absorb extra power produced by the non-dispatchable resources, while the daily average SOC ranges from 48.86% to 65.87% and the final SOC converges to a value close to 50%, regardless of the initial SOC considered. Full article

(This article belongs to the Special Issue Advances in Modeling Methods for Battery Life Prediction and Performance Evaluation)

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16 pages, 1308 KiB

Open AccessArticle

Transferability of a Battery Cell End-of-Life Prediction Model Using Survival Analysis

by Maya Santhira Sekeran, Milan Živadinović and Myra Spiliopoulou

Energies 2022, 15(8), 2930; https://doi.org/10.3390/en15082930 - 15 Apr 2022

Cited by 2 | Viewed by 2307

Abstract

Electric vehicles are increasingly becoming the vehicle of choice in today’s environmentally conscious society, and the heart of an electric vehicle is its battery. Today, lithium-ion batteries are mainly used to power electric vehicles for its increased energy storage density and longevity. However, in order to estimate battery life, long and costly battery testing is required. Therefore, there is a need to investigate efficient ways that could reduce the amount of testing required by reusing existing knowledge of aging patterns from different kinds of battery chemistry. This work aims to answer two research questions. The first addresses the challenge of battery cell testing data that contain battery cells that do not reach the End-of-Life (EOL) threshold by the time the testing has been completed. For this challenge, we propose to implement survival analysis that is able to handle incomplete data or what is referred to as censored data. The second addresses how to reuse a model trained on one type of battery cell chemistry to predict the EOL of another battery cell chemistry by implementing transfer learning. We develop a workflow to implement a prediction model for one type of battery cell chemistry and to reuse this pre-trained model to predict the EOL for another type of battery cell chemistry. Full article

(This article belongs to the Special Issue Advances in Modeling Methods for Battery Life Prediction and Performance Evaluation)

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10 pages, 2613 KiB

Open AccessBrief Report

Thévenin’s Battery Model Parameter Estimation Based on Simulink

by Giulio Barletta, Piera DiPrima and Davide Papurello

Energies 2022, 15(17), 6207; https://doi.org/10.3390/en15176207 - 26 Aug 2022

Cited by 14 | Viewed by 4935

Abstract

Lithium-ion batteries (LIB) proved over time to be one of the best choices among rechargeable batteries. Their small size, high energy density, long life, and low maintenance need make them a prominent candidate for the role of the most widespread energy storage system. They have the potential to monopolize the green technology sector. An accurate definition of the parameters defining the behaviour of the battery in different operating conditions is thus essential, as their knowledge proves crucial in certain fields such as those that involve electric vehicles. This paper proposes the estimation of the values of the parameters of the Thévenin equivalent circuit of a LIB commercial cell. Experimental data obtained through constant current charge/discharge cycles are analysed through a Simulink model, and results are obtained as a function of the state of charge (SOC) for a fixed and controlled temperature value. The results achieved with the proposed model can monitor the salient parameters of the equivalent circuit with an error between 7 and 10%. Full article

(This article belongs to the Special Issue Advances in Modeling Methods for Battery Life Prediction and Performance Evaluation)

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