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Batteries
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Advanced Intelligent Management Technologies of New Energy Batteries
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Advanced Intelligent Management Technologies of New Energy Batteries

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: 30 September 2026 | Viewed by 5844

Editors

Dr. Sijia Yang

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Guest Editor
School of Intelligent Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
Interests: evaluation of the state of new energy batteries; intelligent information processing; big data; machine learning
Prof. Dr. Zeyu Chen

E-Mail Website
Guest Editor
School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
Interests: electric vehicle; lithium-ion battery
Special Issues, Collections and Topics in MDPI journals
Dr. Jichao Hong

E-Mail Website1 Website2
Guest Editor
School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: integrated design of new energy power storage systems; application of big data analytics; intelligent safety management throughout the entire lifecycle
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

New energy batteries are pivotal for sustainable energy storage solutions, from electric vehicles to grid storage. Their reliable and safe operation hinges on accurate state monitoring and prediction. However, challenges such as nonlinear degradation mechanisms, varied/extreme operating conditions, and limited on-board computing resources hinder the accuracy and robustness of existing methods. This Special Issue aims to gather cutting-edge research advances in the state monitoring and prediction of new energy batteries, with a focus on fundamental challenges and innovative solutions in sensor technology, data-driven algorithms, electrochemical modelling and hybrid methodologies, hoping to bridge the lab-scale innovations and on-board/grid-scale applications. We encourage submissions exploring both theoretical breakthroughs and empirical studies to foster a comprehensive understanding of battery behavior and predictive capabilities. The scope includes state estimation (state-of-charge, state-of-health, state-of-power, state-of-energy, state-of-safety, remaining useful life, etc.), fault diagnosis and their prediction/prognosis across diverse new energy battery chemistries (lithium-ion, sodium-ion, fuel cell, flow batteries, etc.).

Potential topics include, but are not limited to, the following:

  • Advanced SOC, SOH, SOP, SOE and SOS estimation algorithms.
  • Fault diagnosis and prognosis for new energy batteries.
  • Multi-physics coupling modeling for battery state estimation/prediction.
  • Novel sensing technologies and multi-sensor data fusion for new energy battery monitoring.
  • Physics-informed artificial intelligentmodels for battery state estimation/prediction.
  • Low-computational-cost algorithms for on-board battery management systems.

Dr. Sijia Yang
Prof. Dr. Zeyu Chen
Dr. Jichao Hong
Guest Editors

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

  • lithium-ion battery
  • fuel cells
  • lithium–sulfur battery
  • solid-state battery
  • flow battery
  • energy storage systems
  • electrical vehicles
  • battery management systems
  • SOX (SOC, SOH, SOP, SOE, SOS, SOT) estimation and prediction
  • lifetime prediction
  • battery safety diagnostics and prognostics
  • fault diagnosis
  • anomaly detection
  • artificial intelligence
  • physics-guided modeling

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (8 papers)

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Research

23 pages, 5879 KB  
Article
BATWO: Bayesian Adaptive Time Window Optimization for Feature Extraction in SOH Estimation of Li-Ion Batteries Under Dynamic Operating Conditions
by Sijia Yang, Jingjing Zhang, Jichao Hong, Zhaolin Yuan, Lifan Wang, Shanshan Guo and Shihan Ge
Batteries 2026, 12(8), 296; https://doi.org/10.3390/batteries12080296 - 8 Aug 2026
Viewed by 207
Abstract
Accurate state of health (SOH) estimation of lithium-ion batteries is critical to the reliability of electric vehicles. However, under dynamic operating conditions, conventional feature extraction based on fixed time window often exhibits poor generalization, as it fails to account for the multi-timescale parameter [...] Read more.
Accurate state of health (SOH) estimation of lithium-ion batteries is critical to the reliability of electric vehicles. However, under dynamic operating conditions, conventional feature extraction based on fixed time window often exhibits poor generalization, as it fails to account for the multi-timescale parameter couplings inherent in the non-stationary voltage responses. To address this issue, this paper proposes a Bayesian Adaptive Time Window Optimization (BATWO) framework for feature extraction in battery SOH estimation. Within this framework, the time window length is treated as a learnable structural parameter and is adaptively optimized via Bayesian optimization to identify the most informative observation timescale for extracting degradation-sensitive statistical features under given operating conditions. Evaluations on a cycle-aging dataset containing 69 lithium-ion battery samples subjected to distinct dynamic operating profiles show that the optimal time window lengths vary significantly, ranging from 500 s to 27,630 s. The BATWO framework achieves an average root-mean-square error (RMSE) of 2.07% and a mean absolute error (MAE) of 1.45%, outperforming the best fixed time window strategy by reducing the RMSE and MAE by 2.35% and 2.68%, respectively. Moreover, compared with LSTM- and Transformer-based models without feature extraction, the BATWO framework reduces training time by over 97%. These results highlight the superior generalization capability and computational efficiency of the BATWO framework, demonstrating its great potential for practical deployment in battery management system. Full article
(This article belongs to the Special Issue Advanced Intelligent Management Technologies of New Energy Batteries)
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33 pages, 6183 KB  
Article
State of Charge Estimation for Both Electric Bus and Passenger Vehicles with Different Battery Types Using Multi-Instance Learning
by Ibrahim Atakan Kubilay and Derya Birant
Batteries 2026, 12(7), 266; https://doi.org/10.3390/batteries12070266 - 21 Jul 2026
Viewed by 253
Abstract
State of charge (SoC) estimation for an electric vehicle (EV) is critical for range estimation, preventing overcharging or undercharging, energy management, and battery lifespan. The current studies are typically based on single-instance learning, focus on a specific battery chemistry or a single vehicle [...] Read more.
State of charge (SoC) estimation for an electric vehicle (EV) is critical for range estimation, preventing overcharging or undercharging, energy management, and battery lifespan. The current studies are typically based on single-instance learning, focus on a specific battery chemistry or a single vehicle type, rely on controlled Lab conditions, and often lack explainability mechanisms. To overcome all these limitations, this paper proposes a more practically applicable and explainable SoC estimation framework that jointly addresses vehicle diversity (bus and passenger EVs), battery chemistry variability (Nickel Cobalt Manganese and Lithium Iron Phosphate), collective battery dynamics, and real-world on-road operational uncertainties within a unified learning architecture. This study introduces MIL-LGBM, a specialized method that integrates Multiple Instance Learning with Light Gradient Boosting Machine to effectively capture the complex nonlinear behavior of battery SoC while maintaining a low computational footprint. An experimental study conducted on a real-world dataset demonstrated that the proposed framework achieved a 0.799 MAE for passenger vehicles across a wide range of on-road driving conditions. Full article
(This article belongs to the Special Issue Advanced Intelligent Management Technologies of New Energy Batteries)
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20 pages, 17118 KB  
Article
A Hybrid Transformer Network Optimized by Kalman Filter for State of Health Prediction on Lithium-Ion Battery
by Lei Xu, Peng Sun and Nan Zhou
Batteries 2026, 12(7), 251; https://doi.org/10.3390/batteries12070251 - 13 Jul 2026
Viewed by 406
Abstract
State of Health (SOH) is a critical metric for evaluating the efficient and reliable operation of lithium-ion batteries (LIBs), although it cannot be directly measured. Accurate SOH prediction throughout the entire lifecycle of LIBs remains a significant challenge, primarily due to severe signal [...] Read more.
State of Health (SOH) is a critical metric for evaluating the efficient and reliable operation of lithium-ion batteries (LIBs), although it cannot be directly measured. Accurate SOH prediction throughout the entire lifecycle of LIBs remains a significant challenge, primarily due to severe signal fluctuations and complex degradation mechanisms. In this paper, a novel hybrid Transformer-based architecture for SOH prediction is introduced, termed KF–SAMformer–GRU, which integrates a Kalman filter (KF) optimizer, a sharpness-aware minimization Transformer (SAMformer) model, and a multi-layer gated recurrent unit (GRU). To overcome the challenges of multivariate long-term forecasting, we innovatively integrate SAMformer to extract robust feature indicators, actively mitigating data distribution shifts via sharpness-aware minimization. Furthermore, reversible instance normalization (RevIN) is first introduced to tackle non-stationarity in multi-source datasets, effectively eliminating uncertainty and significantly boosting generalization capability. Complementing this, the KF mechanism is uniquely employed to fuse multi-dimensional features, reducing computational overhead while accelerating training. Finally, the multi-layer GRU precisely refines the mapping between SOH metrics and predicted values. Experimental validation on the NASA and CALCE datasets demonstrates the superiority of our approach, achieving a MAPE of 0.01, an RMSE of 0.91%, and an R2 of 0.99. Notably, the method accurately captures phenomena such as battery capacity regeneration, exhibiting superior performance at peaks and valleys while maintaining high computational efficiency. Full article
(This article belongs to the Special Issue Advanced Intelligent Management Technologies of New Energy Batteries)
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22 pages, 12382 KB  
Article
State of Charge Estimation of Lithium-Ion Batteries Using the Window Attention Sinks Transformer
by Chang Liu, Zhifeng Zheng and Guodong Xu
Batteries 2026, 12(7), 234; https://doi.org/10.3390/batteries12070234 - 28 Jun 2026
Viewed by 437
Abstract
Lithium-ion batteries are the core energy storage devices for electric vehicles, and accurate state of charge (SOC) estimation is critical to ensuring their safe and reliable operation. Most existing SOC estimation methods are only suitable for constant-temperature scenarios and cannot adapt to the [...] Read more.
Lithium-ion batteries are the core energy storage devices for electric vehicles, and accurate state of charge (SOC) estimation is critical to ensuring their safe and reliable operation. Most existing SOC estimation methods are only suitable for constant-temperature scenarios and cannot adapt to the dynamic temperature variations in actual charging and discharging processes. To address the issue of insufficient estimation accuracy under complex conditions such as high and low temperatures, this study proposes a Window Attention Sinks Transformer (WASFormer) model. Based on the PatchTST framework, the model integrates Rotary Positional Encoding (RoPE) and Window Attention Sinks (WAS) mechanisms, and combines Huber Loss with Reversible Instance Normalization (RevIN) to establish a full-chain robustness enhancement scheme from feature preprocessing to loss optimization, which effectively suppresses the interference of noise and distribution shift on estimation stability. Comparative experiments, generalization tests, and ablation studies under various temperatures and working conditions show that the proposed model achieves higher estimation accuracy, stronger generalization ability, and robustness. It provides an effective and stable new approach for high-precision SOC estimation of lithium-ion batteries over a wide temperature range and under complex operating conditions. Full article
(This article belongs to the Special Issue Advanced Intelligent Management Technologies of New Energy Batteries)
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21 pages, 5242 KB  
Article
A Three-Stage Reaction-Process-Corrected Equivalent Circuit Model for Predicting External Short-Circuit Current in Lithium-Ion Batteries
by Xingzhen Zhou, Chenhui Gao, Weige Zhang, Caiping Zhang, Qinhe Huang, Lei Zhang, Yusheng Li, Ling Chen, Dongzhong Hu and Jinhan Qiu
Batteries 2026, 12(6), 224; https://doi.org/10.3390/batteries12060224 - 21 Jun 2026
Viewed by 427
Abstract
Accurate prediction of external short-circuit (ESC) current is important for battery safety analysis and protection design, but conventional equivalent circuit models have difficulty reproducing the strongly nonlinear current evolution under ESC conditions. This study proposes a reaction-process-corrected second-order RC model for ESC current [...] Read more.
Accurate prediction of external short-circuit (ESC) current is important for battery safety analysis and protection design, but conventional equivalent circuit models have difficulty reproducing the strongly nonlinear current evolution under ESC conditions. This study proposes a reaction-process-corrected second-order RC model for ESC current prediction, based on ESC experiments on a 37 Ah commercial NCM pouch cell at different initial SOCs. The ESC process is described by three successive stages: bottleneck control, concentration-difference control, and separator pore closure. To represent the transport-related resistance deviation during this process, an additional correction resistance Rx and a queued-charge descriptor Q are introduced into the equivalent circuit framework. A segmented closed-loop simulation strategy is then developed to update Rx and predict the ESC current. Using the 50% SOC case as an unseen validation case, the proposed model captures the main nonlinear characteristics of ESC current, including rapid initial decay, secondary rebound, and subsequent attenuation. The proposed framework improves the physical interpretability of equivalent-circuit-based ESC simulation while retaining engineering simplicity, providing a practical approach for safety-boundary assessment and protection-oriented battery system design. Full article
(This article belongs to the Special Issue Advanced Intelligent Management Technologies of New Energy Batteries)
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17 pages, 3915 KB  
Article
State of Health Estimation for Lithium-Ion Batteries Based on Alternating Electrical Signals Within a Specific Frequency Range
by Bo Rao, Jinqiao Du, Jie Tian, Weige Zhang, Xinyuan Fan and Tianrun Yu
Batteries 2026, 12(5), 153; https://doi.org/10.3390/batteries12050153 - 24 Apr 2026
Viewed by 711
Abstract
State of Health (SOH) estimation of lithium-ion batteries is a critical and challenging requirement in advanced battery management technologies. As an important parameter, battery impedance contains significant electrochemical information that can reflect the state of health of batteries. In this study, a SOH [...] Read more.
State of Health (SOH) estimation of lithium-ion batteries is a critical and challenging requirement in advanced battery management technologies. As an important parameter, battery impedance contains significant electrochemical information that can reflect the state of health of batteries. In this study, a SOH estimation method is proposed based on alternating electrical signals. First, an aging test was carried out using commercial 18650-type batteries. Considering the current uncertainty in practical applications, tests under different discharge conditions were conducted to obtain the capacity and wide frequency band impedance data of each battery throughout its life cycle. Then, important features at specific frequencies were extracted from the impedance data, and an interpretable analysis of the features was performed using the distribution of relaxation times (DRTs). Finally, the impedance features were combined with the Gaussian process regression algorithm in machine learning to estimate and validate the SOH. The results show that using fixed-frequency impedance features can achieve accurate estimation. The average value of the maximum absolute error of each battery under different working conditions can be controlled within 1.59%. With the development of embedded chips and online measurement technology, battery management systems can obtain important impedance features by applying alternating electrical signals within a certain frequency range, thus achieving online estimation of SOH. Full article
(This article belongs to the Special Issue Advanced Intelligent Management Technologies of New Energy Batteries)
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23 pages, 3020 KB  
Article
A State of Health Estimation Method for Lithium-Ion Battery Packs Using Two-Level Hierarchical Features and TCN–Transformer–SE
by Chaolong Zhang, Panfen Yin, Kaixin Cheng, Yupeng Wu, Min Xie, Guoqing Hua, Anxiang Wang and Kui Shao
Batteries 2026, 12(4), 123; https://doi.org/10.3390/batteries12040123 - 1 Apr 2026
Viewed by 1628
Abstract
This study proposes a novel state of health (SOH) estimation method by extracting two-level hierarchical features linked to fundamental degradation mechanisms. At the module level, the length of the incremental power curve during constant current charging is extracted, capturing cumulative effects of subtle [...] Read more.
This study proposes a novel state of health (SOH) estimation method by extracting two-level hierarchical features linked to fundamental degradation mechanisms. At the module level, the length of the incremental power curve during constant current charging is extracted, capturing cumulative effects of subtle changes. At the cell level, a combined temperature-weighted voltage inconsistency curve is constructed. The state of charge (SOC) at its distinct knee point within the high-SOC range is a key indicator, signifying the accelerated failure stage where polarization and thermoelectric feedback intensify. This knee-point SOC quantitatively reflects the degree of SOH degradation, making it a valid feature for accurate SOH estimation. The proposed Temporal Convolutional Network–Transformer–Squeeze-and-Excitation (TCN–Transformer–SE) model assigns weights to these features via Squeeze-and-Excitation (SE) and uses Temporal Convolutional Network (TCN) and Transformer branches for parallel local and global temporal decisions. Aging experiments demonstrate the method’s superiority through multi-feature comparison, ablation studies, and benchmark evaluation, achieving a maximum mean absolute error (MAE) of 0.0031, a root mean square error (RMSE) of 0.0038, a coefficient of determination (R2) of 0.9937 and a mean absolute percentage error (MAPE) of 0.3820. The work provides a fusion estimation framework with enhanced interpretability grounded in electrochemical analysis. Full article
(This article belongs to the Special Issue Advanced Intelligent Management Technologies of New Energy Batteries)
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10 pages, 1121 KB  
Article
Research on the Active Safety Warning Technology of LIBs Thermal Runaway Based on FBG Sensing
by Yanli Miao, Xiao Tan, Chenying Li, Jianjun Liu, Ling Sa, Xiaohan Li, Zongjia Qiu and Zhichao Ding
Batteries 2026, 12(3), 110; https://doi.org/10.3390/batteries12030110 - 23 Mar 2026
Viewed by 897
Abstract
Lithium-ion batteries (LIBs) may experience thermal runaway (TR) under thermal abuse conditions, posing significant safety risks to energy storage systems, electric vehicles, and portable electronics. To ensure the safety of LIB-powered applications, developing an effective TR early warning method is crucial. This study [...] Read more.
Lithium-ion batteries (LIBs) may experience thermal runaway (TR) under thermal abuse conditions, posing significant safety risks to energy storage systems, electric vehicles, and portable electronics. To ensure the safety of LIB-powered applications, developing an effective TR early warning method is crucial. This study employs polyimide-coated femtosecond fiber Bragg grating (FBG) sensors to investigate TR characteristics in 18,650 LIBs (LiNi1/3Mn1/3Co1/3O2/graphite), including TR onset temperature determination and the evolution of temperature and radial strain at different states of charge (SOCs). Compared with existing studies, the polyimide-coated femtosecond FBGs employed here offer superior breakage resistance and high-temperature tolerance, enabling more precise temperature and strain measurements. For radial strain monitoring obtained during high-temperature-induced LIBs thermal runaway experiments, temperature compensation was achieved using polyimide-coated femtosecond FBG temperature sensors, yielding higher-accuracy strain evolution profiles. Experimental results demonstrate that the higher-SOC LIBs exhibit more severe TR eruptions, with 1.76× higher peak temperatures and 1.3× greater mass loss than low-SOC LIBs. The proposed scheme pioneers an new approach to effective active safety warning of LIBs thermal runaway. Full article
(This article belongs to the Special Issue Advanced Intelligent Management Technologies of New Energy Batteries)
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