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10th Anniversary of Batteries: Battery Health, Aging and Degradation Mechanisms

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A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Energy Storage System Aging, Diagnosis and Safety".

Deadline for manuscript submissions: 20 August 2026 | Viewed by 12990

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Prof. Dr. Pascal Venet

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Department of the Ampère Laboratory, Claude Bernard University Lyon 1, 69100 Villeurbanne, France
Interests: characterization; modeling; reliability; aging and diagnosis of electric energy storage system (batteries, supercapacitors, capacitors)
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Special Issue Information

Dear Colleagues,

The Special Issue “10th Anniversary of Batteries: Battery Heath, Aging and Degradation Mechanisms” examines how battery performance changes over time, focusing on the factors driving aging, the degradation mechanisms involved, and methods of monitoring, assessing, or mitigating these effects.

Understanding these elements is crucial for improving battery design and enhancing its reliability, efficiency, and lifespan.

Key topics of interest include the following:

Degradation Mechanisms: Study of the physico-chemical processes responsible for capacity loss, increased internal resistance, and reduced battery lifespan;
Performance, Reliability, and State of Health (SoH): Research on how aging affects key performance parameters of batteries, methods for estimating and predicting their state of health, and strategies to enhance their reliability and operational efficiency;
Safety Considerations: Analysis of how degradation impacts battery safety, including the risks of thermal runaway and the implementation of mitigation measures to ensure safe operation.

In celebration of the journal’s 10th anniversary, this Special Issue highlights advances in battery health and aging. We invite researchers, industry professionals, and academics to submit feature papers that present their latest findings, share insights, and discuss future directions in the field. By addressing these critical aspects, this Special Issue aims to contribute to the development of more durable, efficient, and safe battery technologies.

Prof. Dr. Pascal Venet
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Batteries is an international peer-reviewed open access monthly 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 2700 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 aging
degradation mechanisms
capacity loss
internal resistance
battery lifespan
performance and reliability
state of health (SoH)
battery safety
electrochemical degradation
aging prediction models

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Published Papers (9 papers)

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Research

35 pages, 3080 KB

Open AccessArticle

Experimental Multi-Metric Health Assessment of Second-Life Electric Vehicle Batteries for Reuse Pathway Classification

by Md Sabbir Hossen, Gobbi Ramasamy, Ngu Eng Eng and Marran Al Qwaid

Batteries 2026, 12(7), 265; https://doi.org/10.3390/batteries12070265 - 21 Jul 2026

Viewed by 268

Abstract

Second-life electric vehicle (EV) batteries are increasingly recognized as valuable resources for stationary energy storage. However, the heterogeneous degradation of retired batteries makes reliable and application-oriented reuse decisions challenging. Existing studies primarily focus on battery health estimation or degradation characterization, while limited attention has been given to systematically translating experimentally measured health indicators into practical second-life deployment decisions. To address this gap, this study proposes an experimental multi-metric battery health assessment and decision-support framework for application-oriented screening and reuse pathway allocation of retired EV batteries. A total of 91 s life lithium-ion battery cells were experimentally characterized through standardized laboratory charge–discharge testing. Multiple complementary health indicators, including State of Health (SoH), discharge capacity, round-trip energy efficiency, and voltage–current time-series characteristics, were extracted and statistically analyzed to evaluate residual battery performance and degradation behavior. The experimental results reveal substantial variability among retired batteries, with SoH values ranging from approximately 22% to 96%, while more than half of the tested cells exhibit SoH below 60%. Furthermore, batteries with comparable SoH frequently demonstrate different energy efficiencies, indicating that capacity retention alone is insufficient for reliable second-life battery assessment. Building upon these findings, a transparent rule-based decision-support framework is developed to map experimentally measured battery health indicators to application-oriented reuse pathways, including grid-support systems, residential energy storage, backup applications, and recycling. The proposed framework establishes a practical bridge between laboratory battery characterization and deployment-oriented second-life decision-making, providing an interpretable and experimentally grounded methodology for scalable battery screening and sustainable reuse planning. Full article

(This article belongs to the Special Issue 10th Anniversary of Batteries: Battery Health, Aging and Degradation Mechanisms)

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14 pages, 6584 KB

Open AccessArticle

Investigating the Correlation Between Mechanical Impact and Long Term Performance Degradation in Li-Ion Batteries

by John Sherman and Anthony Bombik

Batteries 2026, 12(6), 217; https://doi.org/10.3390/batteries12060217 - 15 Jun 2026

Viewed by 281

Abstract

Lithium-ion batteries (LIBs) are subject to mechanical abuse both in electric vehicles and consumer electronic applications when dropped, which can lead to capacity degradation even if the cells survive the impact. This study investigates the impact of mechanical damage on the electrochemical performance of LIBs, focusing on capacity retention and internal resistance changes. The batteries were subjected to dynamic mechanical impact using varying impact energies (3J, 5J, and 7J) while measuring internal resistance and capacity before and after the impact. Hybrid Pulse Power Characterization (HPPC) was employed to assess internal resistance and capacity degradation across multiple cycles. Our results demonstrate that even minor mechanical damage can cause significant performance decay, especially after several cycles. The study also reveals that the state of charge (SOC) prior to impact has a minimal effect on the survival rate of the cells but influences the extent of damage observed. Post-impact analysis using optical microscopy indicates structural damage, including separator tears and delamination, contributing to capacity fade. This work highlights the importance of considering intermediate mechanical damage in LIB safety and performance assessments. Full article

(This article belongs to the Special Issue 10th Anniversary of Batteries: Battery Health, Aging and Degradation Mechanisms)

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14 pages, 5402 KB

Open AccessArticle

Electrode-Level Emulation of Temperature Impact in Commercial Li-Ion Batteries

by Matthieu Dubarry, Alexa Fernando and David Beck

Batteries 2026, 12(5), 175; https://doi.org/10.3390/batteries12050175 - 16 May 2026

Cited by 1 | Viewed by 485

Abstract

Temperature affects the battery voltage response, and it is essential to take this influence into consideration for diagnosis purposes, as it could be misinterpreted for degradation. Temperature affects cell kinetics, and a good proxy to emulate this impact is to use electrode data at different C rates. This work further validates this concept by analyzing the relationship between temperature and rate at the electrode level for commercial graphite//LiFePO₄ and (silicon, graphite)//LiNi_0.8Mn_0.1Co_0.1O₂ cells. It will be shown that excellent emulation accuracy for both the voltage response and the capacity retention can be obtained for temperatures varying between −14 °C and 55 °C. Full article

(This article belongs to the Special Issue 10th Anniversary of Batteries: Battery Health, Aging and Degradation Mechanisms)

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33 pages, 7680 KB

Open AccessArticle

RUL Prediction in LFP Batteries: Comparison of Gompertz, LSTM and Gompertz-Informed LSTM Models for Interpretability and Accuracy

by Yuri Njathi, Ciira wa Maina and Edwell T. Mharakurwa

Batteries 2026, 12(5), 162; https://doi.org/10.3390/batteries12050162 - 7 May 2026

Viewed by 1090

Abstract

Lithium iron phosphate batteries have seen a recent rise in usage in electric vehicles and battery energy storage systems. For these applications, reliability is of paramount importance, influences long-term adoption and high return on investment, especially regarding battery replacement. Remaining Useful Life (RUL) prediction is at the core of avoiding unexpected failure and enabling proactive battery maintenance. Physics-based and data-driven methods have been explored by researchers, whilst Physics-Informed Neural Networks (PINNs) can combine their strengths in estimating battery RUL. This paper investigates the integration of the Gompertz function, an inherently interpretable white-box model, into Long Short-Term Memory (LSTM) networks to follow the physical laws of degradation, capture downward monotonic behavior and long-term dependencies from data resulting in Gompertz-Informed LSTMs (GILSTMs). Pure LSTMs are regarded as black box systems and critical infrastructure operators such as battery energy storage system (BESS) operators may refrain from using such systems. Gray-box models such as GILSTMs may get over this hurdle by increasing model interpretability and helping industry adopters know when they will benefit from data-driven modeling. This study explores two GILSTM architectures. The first uses an LSTM to predict Gompertz parameters, which are then converted into RUL via the inverse Gompertz equation. The second uses the inverse Gompertz equation as a verification step to cross-check the RUL values generated by the LSTM. The first type of GILSTM was constrained by both a physics loss and an inverse Gompertz layer to predict RUL while the second verified the results of an LSTM, despite that the GILSTMs failed to generalize. The first type of GILSTM achieved an average RMSE of 22.97%, while the second type achieved an average RMSE of 26.99%. The models in this paper are also benchmarked on the first 100 cycles, a current state of art for battery degradation testing. The best overall implementation was an LSTM that predicted RUL by recursively predicting SoH achieving an average RMSE per cycle of 9.18% and a 100th cycle RMSE of 17.02%. This study evaluates the trade-off between the predictive accuracy of black-box LSTMs and physical interpretability of Gompertz models. While pure LSTMs provide superior accuracy, the Gompertz parameters stabilize by 85% SoH. This 85% threshold serves as an interpretable confidence trigger, informing BESS operators when to rely on LSTM RUL forecasts. Full article

(This article belongs to the Special Issue 10th Anniversary of Batteries: Battery Health, Aging and Degradation Mechanisms)

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21 pages, 19856 KB

Open AccessArticle

An Adaptive-Weight Physics-Informed Neural Network Optimized by Grey Wolf Optimizer for Lithium-Ion Battery State of Health Estimation

by Runtong Wang, Jiakang Shen, Shupeng Liu and Hailin Rong

Batteries 2026, 12(4), 115; https://doi.org/10.3390/batteries12040115 - 26 Mar 2026

Cited by 4 | Viewed by 1252

Abstract

Reliable estimation of the State of Health (SOH) in lithium-ion batteries is critical to battery system security and dependability. However, existing Physics-Informed Neural Networks (PINNs) have drawbacks like single-feature physical constraints, rigid fixed-weight fusion of multi-feature constraints and insufficient time-series degradation modeling. To solve these problems, this study proposes an Adaptive-Weight PINN (AW-PINN) optimized by the Grey Wolf Optimizer (GWO) algorithm, which features a dual-LSTM parallel structure and takes incremental capacity peaks and charged capacity as dual physical constraints. A weight generator LSTM adaptively learns weights for monotonicity losses without manual intervention, and GWO globally optimizes physical loss weights to balance data fitting accuracy and prediction physical consistency. Validated on

{LiCoO}_{2}

, NCA, and NCM batteries from CALCE and Tongji University datasets via comparative, ablation, and small-sample experiments, AW-PINN shows superior predictive performance (average RMSE = 0.0076; MAE = 0.0065; MAPE = 0.0072), robustness, and generalization. It integrates battery degradation physics with deep learning, retaining strong fitting capability while enabling physical interpretability. Full article

(This article belongs to the Special Issue 10th Anniversary of Batteries: Battery Health, Aging and Degradation Mechanisms)

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15 pages, 2869 KB

Open AccessArticle

Analysis of the Failure Modes, Mechanisms, and Effects of Potassium Acetate Water-in-Salt Electrolyte-Based Supercapacitor

by Jose Miguel Delgado, Joan Ramon Morante and Jordi Jacas Biendicho

Batteries 2026, 12(4), 111; https://doi.org/10.3390/batteries12040111 - 24 Mar 2026

Viewed by 1154

Abstract

Water-In-Salt (WIS) electrolytes are expected to replace expensive, environmentally harmful organic electrolytes while delivering high voltages and improving system safety. In this study, analysis of the failure modes, mechanisms, and effects of a highly concentrated potassium acetate (KAc) electrolyte was conducted through electrolyte degradation at 2 V in a conventional EDLC carbon-based symmetric configuration. The adopted method provides a simplified yet effective approach for assessing the complexity and interconnectivity of degradation mechanisms in a WIS supercapacitor. The effects analysis included electrochemical stability studies, post-mortem characterizations (SEM-EDS and XPS), low-frequency impedance fitting, and cell reassembly using end-of-life electrodes. Among the failure modes analyzed, electrolyte decomposition and pore blocking exhibit strong physicochemical correlations and high failure rates. Therefore, they should be prioritized in the design of new WIS electrolyte compositions for next-generation energy storage systems. Full article

(This article belongs to the Special Issue 10th Anniversary of Batteries: Battery Health, Aging and Degradation Mechanisms)

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15 pages, 2034 KB

Open AccessArticle

State of Health Estimation of Lithium-Ion Batteries Based on Voltage Data Segment Hybrid Model

by Zhenhan Zou, Xiangyang Xia, Chaofeng Zhang, Jiahui Yue, Boyan Xia and Caibo Zhou

Batteries 2026, 12(3), 83; https://doi.org/10.3390/batteries12030083 - 28 Feb 2026

Viewed by 983

Abstract

The health state estimation of lithium-ion batteries are the essential issues for the safety of energy storage stations. The important indicators often focus on the battery capacity and internal resistance. However, the measurement of capacity requires a complete charge/discharge cycle, and the measurement of internal resistance requires additional equipment. To solve the above problems, based on the voltage segment under the constant-current discharge condition of lithium-ion battery, this paper takes the sharp voltage drop of the initial discharge segment as a new healthy factor. Furthermore, facing the possibility that the new healthy factor data is polluted by noise, this factor data is reconstructed to reduce noise through multi-order Bezier curve. Subsequently, an empirical degradation hybrid model is constructed with the number of cycles. On this basis, the battery healthy state is defined by voltage segment and a new healthy state estimation model is proposed. The feasibility and effectiveness of the proposed degradation model and estimation model are verified by the aging data published by NASA and experimental platform. Full article

(This article belongs to the Special Issue 10th Anniversary of Batteries: Battery Health, Aging and Degradation Mechanisms)

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20 pages, 4124 KB

Open AccessArticle

Experimental Investigation of the Impact of V2G Cycling on the Lifetime of Lithium-Ion Cells Based on Real-World Usage Data

by George Darikas, Mehmet Cagin Kirca, Nessa Fereshteh Saniee, Muhammad Rashid, Ihsan Mert Muhaddisoglu, Truong Quang Dinh and Andrew McGordon

Batteries 2026, 12(1), 22; https://doi.org/10.3390/batteries12010022 - 8 Jan 2026

Cited by 1 | Viewed by 2102

Abstract

This work investigated the impact of vehicle-to-grid (V2G) cycling on the service life of lithium-ion cells, using real-world V2G data from commercial electric vehicle (EV) battery chargers. Commercially available cylindrical lithium-ion cells were subjected to long-term storage and V2G cycling under varying state of charge (SOC), depth of discharge (DOD), and temperature conditions. The ageing results demonstrate that elevated temperature (40 °C) is the dominant factor accelerating degradation, particularly at a high storage SOC (>80% SOC) and increased cycle depths (30–80% SOC, 30–95% SOC). A comparison between V2G cycling and calendar ageing over a similar storage period revealed that shallow V2G cycling (30–50% SOC) leads to comparable capacity fade to storage at a high SOC (≥80% SOC). The comparative analysis indicated that 62% of a full equivalent cycle (FEC) of V2G cycling can be achieved daily, without compromising the cell’s lifetime, demonstrating the viability of V2G adoption during EV idle/charging periods, which can offer potential operational benefits in terms of cost reduction and emissions savings. Furthermore, this work introduced the concept of a V2X capability metric as a novel cell-level specification, along with a corresponding experimental evaluation method. Full article

(This article belongs to the Special Issue 10th Anniversary of Batteries: Battery Health, Aging and Degradation Mechanisms)

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25 pages, 3252 KB

Open AccessArticle

Development of a Degradation Model for Lifespan Prediction: A Case Study on Grid-Scale Battery Energy Storage Systems in Thailand

by Nipon Ketjoy, Yodthong Mensin, Pornthip Mensin, Malinee Kaewpanha, Sunisa Khakhu, Chaphamon Chantarapongphan and Shahril Irwan Sulaiman

Batteries 2025, 11(11), 429; https://doi.org/10.3390/batteries11110429 - 20 Nov 2025

Cited by 5 | Viewed by 4559

Abstract

In this paper, we present a model for calculating the State of Health (SOH) of battery energy storage systems (BESSs) and battery capacity percentage, specifically tailored for grid-scale applications in Thailand. Unlike conventional models that rely on controlled laboratory data, the proposed approach uses actual operating temperature data for both development and validation, enabling a more correct assessment of battery performance under the high-temperature conditions typical of tropical climates. A set of coefficients derived from real operating data was incorporated, and the SOH results deviated by only 0.05% from theoretical values, proving high predictive accuracy beyond laboratory settings. Our findings revealed that capacity degradation rates in Thailand are approximately 20–60% higher than under the best conditions. Over a 10-year warranty period, battery capacity declined to approximately 80% at the lowest temperature range, 60% at the average range, and 40% at the highest range. By calculating both SOH and remaining capacity, the model provides a practical tool for lifespan prediction and system planning. Based on these findings, it is recommended that thermal management systems support battery operating temperatures between 25 and 35 °C and limit cooling losses below 10%, thereby mitigating energy yield degradation and ensuring efficient BESS operation. These results highlight the importance of incorporating real environmental data into degradation modeling. Future studies should include long-term monitoring of operating temperatures and cooling demand, economic analyses to enhance operational efficiency, and evaluation of external heat loads, particularly from solar radiation, to further refine predictions for tropical climates. Full article

(This article belongs to the Special Issue 10th Anniversary of Batteries: Battery Health, Aging and Degradation Mechanisms)

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