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

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: 23 January 2025 | Viewed by 7536

Special Issue Editor

Dr. Md Sazzad Hosen

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The widespread use of batteries as the most common energy storage systems in automotive and consumer electronics have made it an integral part of our daily business. The crucial concern like battery lifetime, thus, requires principal attention that is often tackled by modeling works. Researchers have made remarkable achievements to develop models that could predict the battery lifetime, state of health (SoH), remaining useful life, etc. outlining the aging behavior. Numerous modeling methodologies from physics-based to black-box types have enriched the prediction modeling accuracy by several folds.

This Special Issue highlights the research efforts towards advanced lifetime prediction methodologies and/or algorithms development works in terms of contributions (research/perspective/review articles). This is the second volume of the series following up the excellent collection of the works in the first issue. Novel methodologies and characterization techniques to predict battery aging could also be included for battery diagnosis and prognosis from cell to pack level. The authors are encouraged to submit original articles addressing potential but not limited to the following topics.

  • Battery aging and lifetime prediction models
  • Battery state of X (SoC, SoH, SoE, SoP, SoS) estimation
  • Early life and Remaining useful life (RUL) prediction
  • Rest time based or accelerated aging studies
  • Advanced algorithms for on-board predictions
  • Diagnosis and prognosis of battery systems
  • Physics-based degradation modeling
  • Model development using field-data (e-mobility & stationary)
  • Machine learning or data-driven battery predictions
  • Review of state-of-the-art battery modeling methodologies

Dr. Md Sazzad Hosen
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

  • lifetime modeling
  • aging prediction
  • battery state 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

Published Papers (5 papers)

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Research

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12 pages, 1182 KiB  
Article
An Arrhenius-Based Simulation Tool for Predicting Aging of Lithium Manganese Dioxide Primary Batteries in Implantable Medical Devices
by Mahsa Doosthosseini, Mahdi Khajeh Talkhoncheh, Jeffrey L. Silberberg and Hamed Ghods
Energies 2024, 17(21), 5392; https://doi.org/10.3390/en17215392 - 30 Oct 2024
Viewed by 383
Abstract
This article presents a novel aging-coupled predictive thermo-electrical dynamic modeling tool tailored for primary lithium manganese dioxide (Li-MnO2) batteries in active implantable medical devices (AIMDs). The aging mechanisms of rechargeable lithium batteries are well documented [...] Read more.
This article presents a novel aging-coupled predictive thermo-electrical dynamic modeling tool tailored for primary lithium manganese dioxide (Li-MnO2) batteries in active implantable medical devices (AIMDs). The aging mechanisms of rechargeable lithium batteries are well documented using computationally intensive physics-based models, unsuitable for real-time onboard monitoring in AIMDs due to their high demands. There is a critical need for efficient, less demanding modeling tools for accurate battery health monitoring and end-of-life prediction as well as battery safety assessment in these devices. The presented model in this article simulates the battery terminal voltage, remaining capacity, temperature, and aging during active discharge, making it suitable for real-time health monitoring and end-of-life prediction. We incorporate a first-order dynamic for internal resistance growth, influenced by time, temperature, discharge depth, and load current. By adopting Arrhenius-type kinetics and polynomial relationships, this model effectively simulates the combined impact of these variables on battery aging under diverse operational conditions. The simulation handles both the continuous micro-ampere-level demands necessary for device housekeeping and periodic high-rate pulses needed for therapeutic functions, at a constant ambient temperature of 37 °C, mimicking human body conditions. Our findings reveal a gradual, nonlinear increase in internal resistance as the battery ages, rising by an order of magnitude over a period of 5 years. Sensitivity analysis shows that as the battery ages and load current increases, the terminal voltage becomes increasingly sensitive to internal resistance. Specifically, at defibrillation events, the ∂V∂R trajectory dramatically increases from 10−12 to 10−8, indicating a fourth-order-of-magnitude enhancement in sensitivity. A model verification against experimental data shows an R2 value of 0.9506, indicating a high level of accuracy in predicting the Li-MnO2 cell terminal voltage. This modeling tool offers a comprehensive framework for effectively monitoring and optimizing battery life in AIMDs, therefore enhancing patient safety. Full article
(This article belongs to the Special Issue Advances in Modeling Methods for Battery Life Prediction and Performance Evaluation (Volume II))
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17 pages, 5634 KiB  
Article
An Enhanced Ageing Model for Solid-State Batteries
by Paolo Scaltrito, Amirmasoud Lanjan and Seshasai Srinivasan
Energies 2024, 17(12), 2896; https://doi.org/10.3390/en17122896 - 13 Jun 2024
Viewed by 735
Abstract
The emphasis in the automotive industry towards sustainable mobility has led to a significant interest in hybrid-electric drive-trains with high energy density batteries. Addressing the needs of this strategy, the battery market is exploring new technologies to improve the safety and lifespan of [...] Read more.
The emphasis in the automotive industry towards sustainable mobility has led to a significant interest in hybrid-electric drive-trains with high energy density batteries. Addressing the needs of this strategy, the battery market is exploring new technologies to improve the safety and lifespan of electric vehicles. To this end, there is a focus on the all-solid-state battery (ASSB) technology for its cycle capabilities. Filling the current void in the literature pertaining to accurate ageing models for ASSBs, in the present work, we propose an enhanced version of the numerical ageing model, originally developed for liquid electrolyte based batteries, to forecast the development of the solid electrolyte interface layer that is the major cause of battery capacity fading. The model has been tested on prototype batteries and reveals an accuracy of 99%. The capacity fade in ASSBs has been investigated under different conditions and the enhanced ageing model has been validated using experimental data from these batteries. The findings suggest that there is potential for solid-state batteries to be commercialized, although significant work is needed to match the manufacturing level of lithium-ion batteries embedded with liquid electrolytes. Full article
(This article belongs to the Special Issue Advances in Modeling Methods for Battery Life Prediction and Performance Evaluation (Volume II))
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13 pages, 8910 KiB  
Article
Intelligent Learning Method for Capacity Estimation of Lithium-Ion Batteries Based on Partial Charging Curves
by Can Ding, Qing Guo, Lulu Zhang and Tao Wang
Energies 2024, 17(11), 2686; https://doi.org/10.3390/en17112686 - 31 May 2024
Viewed by 712
Abstract
Lithium-ion batteries are widely used in electric vehicles, energy storage power stations, and many other applications. Accurate and reliable monitoring of battery health status and remaining capacity is the key to establish a lithium-ion cell management system. In this paper, based on a [...] Read more.
Lithium-ion batteries are widely used in electric vehicles, energy storage power stations, and many other applications. Accurate and reliable monitoring of battery health status and remaining capacity is the key to establish a lithium-ion cell management system. In this paper, based on a Bayesian optimization algorithm, a deep neural network is structured to evaluate the whole charging curve of the battery using partial charging curve data as input. A 0.74 Ah battery is used for experiments, and the effect of different input data lengths is also investigated to check the high flexibility of the approach. The consequences show that using only 20 points of partial charging data as input, the whole charging profile of a cell can be exactly predicted with a root-mean-square error (RMSE) of less than 19.16 mAh (2.59% of the nominal capacity of 0.74 Ah), and its mean absolute percentage error (MAPE) is less than 1.84%. In addition, critical information including battery state-of-charge (SOC) and state-of-health (SOH) can be extracted in this way to provide a basis for safe and long-lasting battery operation. Full article
(This article belongs to the Special Issue Advances in Modeling Methods for Battery Life Prediction and Performance Evaluation (Volume II))
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17 pages, 4921 KiB  
Article
Current Pulse-Based Measurement Technique for Zinc–Air Battery Parameters
by Lin Hu and Xianzhi Xu
Energies 2023, 16(18), 6448; https://doi.org/10.3390/en16186448 - 6 Sep 2023
Viewed by 1546
Abstract
Zinc–air batteries possess advantages such as high energy density, low operational costs, and abundant reserves of raw materials, demonstrating broad prospects for applications in areas like stationary power supplies and emergency power sources. However, despite significant advancements in zinc–air battery technology, a comprehensive [...] Read more.
Zinc–air batteries possess advantages such as high energy density, low operational costs, and abundant reserves of raw materials, demonstrating broad prospects for applications in areas like stationary power supplies and emergency power sources. However, despite significant advancements in zinc–air battery technology, a comprehensive measurement model for zinc–air battery parameters is still lacking. This paper utilizes a gas diffusion model to separately calculate the concentration polarization of zinc–air batteries, decoupling it from electrochemical polarization and ohmic polarization, simplifying the equivalent circuit model of zinc–air batteries into a first-order RC circuit. Subsequently, based on the simplified equivalent circuit model and gas diffusion model, a zinc–air battery parameter measurement technique utilizing current pulse methods is proposed, with predictions made for the dynamic voltage response during current pulse discharges. Validation of this method was conducted through single current pulses and step current pulses. Experimental results demonstrate the method’s capability to accurately measure zinc–air battery parameters and predict the dynamic voltage response. Full article
(This article belongs to the Special Issue Advances in Modeling Methods for Battery Life Prediction and Performance Evaluation (Volume II))
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Review

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21 pages, 910 KiB  
Review
Selecting Suitable Battery Technologies for Untethered Robot
by Tom Verstraten, Md Sazzad Hosen, Maitane Berecibar and Bram Vanderborght
Energies 2023, 16(13), 4904; https://doi.org/10.3390/en16134904 - 23 Jun 2023
Cited by 1 | Viewed by 2505
Abstract
Untethered robots carry their own power supply in the form of a battery pack, which has a crucial impact on the robot’s performance. Although battery technologies are richly studied and optimized for applications such as electric vehicles, computers and smartphones, they are often [...] Read more.
Untethered robots carry their own power supply in the form of a battery pack, which has a crucial impact on the robot’s performance. Although battery technologies are richly studied and optimized for applications such as electric vehicles, computers and smartphones, they are often a mere afterthought in the design process of a robot system. This tutorial paper proposes criteria to evaluate the suitability of different battery technologies for robotic applications. Taking into consideration the requirements of different applications, the capabilities of relevant battery technologies are evaluated and compared. The tutorial also discusses current limitations and new technological developments, pointing out opportunities for interdisciplinary research between the battery technology and robotics communities. Full article
(This article belongs to the Special Issue Advances in Modeling Methods for Battery Life Prediction and Performance Evaluation (Volume II))
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