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Advances in Battery Modelling, Applications, and Technology

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D: Energy Storage and Application".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 2612

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Dr. Simone Barcellona

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Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy
Interests: circuits and systems; theory and applications; power electronic converters and energy storage systems; lithium-Ion battery; battery aging
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Prof. Dr. Lorenzo Codecasa

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Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy
Interests: electric engineering; electro-thermal circuit modeling and simulation; electromagnetic simulation
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Special Issue Information

Dear Colleagues,

Batteries, among various energy storage systems, have garnered attention not only in stationary applications but also in a wide range of mobile applications, spanning from small devices such as smartphones, notebooks, and tablets to larger ones such as electric vehicles and railway traction systems. Over the years, different technologies, such as lead–acid and lithium–ion technologies, have been developed, and ongoing research explores novel technologies. Enhancing the lifecycle involves exploring innovative technologies in battery construction and/or optimizing their operational conditions. Achieving this requires a profound understanding of battery modelling and parameter estimation. Battery modelling can encompass various aspects, including chemical, electrical, thermal, or aging factors, or a combination of these. These models can be implemented in battery management systems, which can properly control the battery operating conditions, ensuring batteries work inside their safe and optimal operating range, limit their degradation, and increase their performance. In particular, the management of the thermal aspect of batteries is crucial for preventing overtemperatures, limiting temperature degradation and possible consequential thermal runaway. The latter can be very dangerous as it can lead to combustion and explosions. Moreover, the thermal battery management system can be useful for balancing the temperature among different battery cells or modules. This way, it is possible to equalize the thermal stress among cells/modules, ensuring the uniform degradation and efficiency of the individual cells/modules.

In light of the above, the scope of this Special Issue is to collect both original research works and review articles on battery models, with a particular focus on the thermal aspect, and their optimized control through battery management systems.

Dr. Simone Barcellona
Prof. Dr. Lorenzo Codecasa
Guest Editors

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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.

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Keywords

energy storage systems
battery technologies
battery applications
battery modelling (chemical, electrical, thermal, aging models)
parameter estimation techniques

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Published Papers (5 papers)

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Research

19 pages, 1447 KB

Open AccessArticle

Robust MILP Optimization of Renewable Power Plants: The Role of BESS Sizing in Uncertainty Mitigation

by Tommaso Dieci, Corrado Maria Caminiti, Matteo Spiller and Marco Merlo

Energies 2026, 19(10), 2467; https://doi.org/10.3390/en19102467 - 21 May 2026

Viewed by 186

Abstract

The reduction of carbon dioxide related to the energy sector is one of the greatest challenges of this century. To ensure a proper transition towards a sustainable electric power system, innovative solutions are fundamental for the efficient integration of renewable energy sources. Hybrid Renewable Energy Systems (HRES) play a crucial role in this scenario; they can ensure a stable and reliable electricity supply thanks to the combination of different renewable technologies, particularly thanks to the integration of storage systems. However, the optimal sizing process of such systems is a complex challenge due to the multiple uncertainties that can be present, involving demand fluctuations and electricity zonal price variations. The aim of this work was to develop a Mixed-Integer Linear Programming (MILP) optimization approach for the robust sizing of a HRES under multiple sources of uncertainty. The developed hybrid model consists of a wind farm, a photovoltaic (PV) plant, a Battery Energy Storage System (BESS), and an industrial load with the entire infrastructure for connection to the national power grid. Additionally, the model includes the capability to manage the over-generation of renewable resources through curtailment mechanisms. The objective of the sizing tool is to minimize the Net Present Cost (NPC) of the plant, while ensuring the reliability of the system. The developed tool can represent a useful assistant for the evaluation of different possible configurations, helping the decision-making process during the design of a HRES. The results will show the best trade-off between economic and reliability aspects, highlighting the impact that the uncertainty has on the optimal size of the plant. In particular, the best configuration analyzed is able to reduce the NPC of more than 50% compared to a plant with a single renewable source. Full article

(This article belongs to the Special Issue Advances in Battery Modelling, Applications, and Technology)

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20 pages, 5481 KB

Open AccessArticle

Cycle Aging Effect on the Inverse Open Circuit Voltage Curve of LiCoO₂ Batteries Under Different Voltage/SOC Conditions

by Simone Barcellona, Silvia Colnago and Lorenzo Codecasa

Energies 2026, 19(10), 2273; https://doi.org/10.3390/en19102273 - 8 May 2026

Viewed by 267

Abstract

Lithium-ion batteries are widely used in applications ranging from portable electronics to electric vehicles and grid energy storage, owing to their high energy density, efficiency, and long lifetime. However, their performance degrades over time due to aging mechanisms such as solid electrolyte interface growth, lithium plating, and electrolyte decomposition, leading to capacity fade and reduced power capability. Accurate state of charge (SOC) estimation is therefore essential for ensuring safe and efficient battery operation, particularly within battery management systems. While many existing methods rely on the direct relationship between open circuit voltage (OCV) and SOC, practical applications require the inverse mapping, i.e., the estimation of SOC from measured OCV values. This inversion is not always straightforward: analytical solutions are only available for simple models, whereas more accurate formulations often require computationally intensive numerical methods. Direct analytical SOC–OCV relationships (inverse OCV–SOC models) provide an effective alternative, enabling simplified SOC estimation without numerical inversion. Previous work proposed a direct generalized Gaussian analytical relationship expressing the absolute state of charge as a function of OCV, thereby simplifying SOC estimation and avoiding numerical inversion, developed and validated on a lithium cobalt oxide battery cycled in the linear region of the OCV curve at constant battery temperature. Building upon this study, the proposed approach was extended to investigate the effects of cycle aging across a wider operating range, considering low, medium, and high voltage/SOC conditions. The model was experimentally validated, at constant battery temperature, on the same type of lithium cobalt oxide batteries through an extensive testing campaign, demonstrating its effectiveness in capturing battery behavior under different operating conditions. Full article

(This article belongs to the Special Issue Advances in Battery Modelling, Applications, and Technology)

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19 pages, 1298 KB

Open AccessArticle

Evidential Deep Learning for Quantification of Uncertainty in Lithium-Ion Batteries Remaining Useful Life Estimation

by Luca Martiri and Loredana Cristaldi

Energies 2026, 19(6), 1513; https://doi.org/10.3390/en19061513 - 18 Mar 2026

Cited by 1 | Viewed by 629

Abstract

Lithium-ion batteries are widely used across diverse applications due to their high energy density, long cycle life, and fast charging capabilities. As battery-powered systems become increasingly critical, accurate estimation of the Remaining Useful Life (RUL) is essential for ensuring reliability, safety, and effective maintenance planning. This work investigates Evidential Deep Learning (EDL) for data-driven RUL estimation and introduces a novel risk-aware loss function designed to enhance both predictive accuracy and uncertainty quantification in the End-of-Life (EoL) region, where precise and trustworthy predictions are most needed. Using a publicly available dataset of lithium iron phosphate (LFP) cells, we benchmark the proposed approach against a baseline Conv–LSTM model, Monte Carlo (MC) Dropout, and Deep Ensembles. The results show that integrating the risk-aware loss into the EDL framework substantially improves the calibration of predictive uncertainty while achieving state-of-the-art accuracy near EoL. Unlike MC Dropout and Deep Ensembles, which exhibit increasing or unstable uncertainty as degradation accelerates, the proposed EDL model demonstrates a consistent reduction in uncertainty and significantly higher reliability in late-stage predictions. The findings indicate that the risk-aware evidential framework offers a reliable and computationally efficient solution for battery RUL estimation, enabling more informed decision-making in both safety-critical and consumer-oriented applications. Full article

(This article belongs to the Special Issue Advances in Battery Modelling, Applications, and Technology)

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20 pages, 8450 KB

Open AccessArticle

Data-Driven Machine Learning Prediction of Impact Failure in Cylindrical Lithium-Ion Batteries

by Bokui Li, Yuhang Zhou, Xuehui Zhou, Zixuan Huang and Xinchun Zhang

Energies 2026, 19(6), 1435; https://doi.org/10.3390/en19061435 - 12 Mar 2026

Viewed by 396

Abstract

The mechanical safety of lithium-ion batteries (LIBs) under dynamic impact has been recognized as a critical concern for electric vehicles. In this study, three experimental dynamic impact datasets of cylindrical LIBs were established through drop-weight tests, with each dataset capturing the effects of indenter geometry, impact repetition, and state of charge (SOC). Using these datasets, six representative machine learning (ML) models—including ANN, SVR, LSTM, TCN, RF, and XGBoost—were evaluated for predicting force–time responses and analyzing failure-related characteristics indicated by the synchronized voltage response. The results indicated that ensemble models (XGBoost and RF) provided the highest predictive accuracy (R² > 0.999) under the tested conditions, while temporal models (LSTM and TCN) effectively captured nonlinear time-dependent behavior. These findings demonstrate that ML-based prediction offers a rapid and reliable means for impact-response assessment and voltage-drop-based failure indication in cylindrical LIBs, supporting early-stage safety screening under the investigated impact conditions. Full article

(This article belongs to the Special Issue Advances in Battery Modelling, Applications, and Technology)

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12 pages, 3631 KB

Open AccessArticle

A Study on the Lithium-Ion Battery Fire Prevention Diagnostic Technique Based on Time-Resolved Partial Discharge Algorithm

by Wen-Cheng Jin, Chang-Won Kang, Soon-Hyung Lee, Kyung-Min Lee and Yong-Sung Choi

Energies 2025, 18(24), 6510; https://doi.org/10.3390/en18246510 - 12 Dec 2025

Cited by 2 | Viewed by 760

Abstract

Lithium-ion batteries are extensively employed in electric vehicles (EVs) and energy storage systems (ESSs) owing to their high energy density, long cyclability, and cost-effectiveness. However, the use of flammable electrolytes makes them inherently susceptible to thermal runaway (TR), which can lead to ignition, explosion, and large-scale fires. Accordingly, early detection of defect internal conditions that precede thermal events is essential for ensuring battery safety. This study proposes a time-resolved partial discharge (TRPD)-based diagnostic method for identifying early electrical precursors of fire hazards in lithium-ion batteries. Both destructive (ex situ) and non-destructive (in situ) experiments were performed to collect defect signal data under physical deformation and accelerated degradation conditions. Through fast fourier transform (FFT) analysis of the acquired signals, specific frequency-domain characteristics associated with micro internal short circuits (MISC) were identified, particularly within the 3.9 MHz, 11.9 MHz, and 19 MHz bands. Defect signals were clearly distinguishable from background common-mode voltage (CMV) noise, confirming the diagnostic sensitivity of the proposed approach. The results demonstrate that the TRPD-based technique enables early recognition of latent insulation degradation and internal short-circuit phenomena before thermal runaway occurs. This work bridges the gap between conventional insulation monitoring and battery safety diagnostics, providing a scalable framework for integrating high-frequency signal analysis into EV and ESS battery management systems for fire prevention. Full article

(This article belongs to the Special Issue Advances in Battery Modelling, Applications, and Technology)

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