Batteries

Research

16 pages, 22701 KB

Open AccessArticle

Research on External Short Circuit Fault Evaluation Method for Li-Ion Batteries Based on Impedance Spectrum Feature Extraction

by Zhongshen Hong, Jinyuan Gao and Yujie Wang

Batteries 2025, 11(12), 437; https://doi.org/10.3390/batteries11120437 - 25 Nov 2025

Viewed by 321

Abstract

Accurate evaluation of the severity of external short-circuit (ESC) faults in li-ion batteries is critical to ensuring the safety and reliability of battery systems. This study proposes a novel ESC fault assessment method based on electrochemical impedance spectroscopy (EIS) and differential feature extraction [...] Read more.

Accurate evaluation of the severity of external short-circuit (ESC) faults in li-ion batteries is critical to ensuring the safety and reliability of battery systems. This study proposes a novel ESC fault assessment method based on electrochemical impedance spectroscopy (EIS) and differential feature extraction from relaxation time distributions. By comparing EIS responses before and after the short circuit, differential curves are constructed, and relevant peak descriptors are extracted to form physically interpretable feature vectors without requiring equivalent circuit modeling. Standardized feature data are further analyzed using principal component analysis (PCA) and K-Means clustering to perform unsupervised classification of fault severity. In addition, a differential evolution algorithm is employed to adaptively optimize the feature weights, enhancing the monotonic correlation between the weighted scores and actual short-circuit durations. The resulting SeverityScore provides an interpretable, mechanism-driven indicator of ESC fault severity. Experimental results demonstrate that the proposed method effectively distinguishes between mild and moderate short-circuit conditions and generalizes well across four independent battery groups. The model, trained on a single group, demonstrates strong robustness by accurately classifying the fault severity for three unseen validation groups. This data-driven framework offers a robust and model-free approach for fault evaluation, providing a promising tool for health monitoring and risk assessment in li-ion batteries. Full article

(This article belongs to the Special Issue Control, Modelling, and Management of Batteries)

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17 pages, 2673 KB

Open AccessArticle

Research on SOC Estimation of Lithium-Ion Battery Based on CA-SVDUKF Algorithm

by Jinrun Cheng, Kuo Yang and Xing Hu

Batteries 2025, 11(12), 435; https://doi.org/10.3390/batteries11120435 - 25 Nov 2025

Viewed by 294

Abstract

Because of the problem that the traditional unscented Kalman filter algorithm (UKF) may terminate the iteration due to the non-positive definite error covariance matrix during state of charge (SOC) estimation of lithium-ion battery, considering the unknown noise and current mutation during the actual [...] Read more.

Because of the problem that the traditional unscented Kalman filter algorithm (UKF) may terminate the iteration due to the non-positive definite error covariance matrix during state of charge (SOC) estimation of lithium-ion battery, considering the unknown noise and current mutation during the actual operation of the battery, an SOC estimation method based on covariance adaptive singular value decomposition unscented Kalman filter (CA-SVDUKF) algorithm was proposed. Based on the singular value decomposition traceless Kalman filtering algorithm, the proposed CA-SVDUKF algorithm introduced an adaptive method of covariance matching to improve the algorithm’s anti-interference capability to unknown noise. Accordingly, an error covariance matrix adaptive method with adaptive scaling factor was proposed, which could reduce the influence of current mutation exerting on the estimated convergence rate. Taking the lithium-ion battery as the research object, the second-order RC equivalent circuit model of the lithium-ion battery was first built, and then the online parameters of the battery were identified. Finally, the CA-SVDUKF algorithm was used to complete the SOC estimation. The algorithm was simulated and verified under three working conditions: ordinary pulse condition, DST working condition, and US06 working condition. The experimental results showed that the algorithm had higher accuracy and stability compared with the traditional extended Kalman filter algorithm (EKF) and the UKF algorithm. The maximum absolute error was less than 0.6%, and the root mean square error was less than 0.3%, which could verify the effectiveness and superiority of the algorithm. Full article

(This article belongs to the Special Issue Control, Modelling, and Management of Batteries)

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19 pages, 4362 KB

Open AccessArticle

Electrode-Resolved Analysis of Lithium Full Cells via OCV-Relaxation Deconvolution

by Yu-Jeong Min and Heon-Cheol Shin

Batteries 2025, 11(11), 415; https://doi.org/10.3390/batteries11110415 - 12 Nov 2025

Viewed by 526

Abstract

We present a time-domain direct current (DC) approach to differentiate positive- (PE) and negative-electrode (NE) contributions from two-electrode full-cell signals in lithium-ion batteries, enabling electrode-resolved diagnostics without specialized instrumentation. The responses of a LiNi_0.8Co_0.1Mn_0.1O₂ (PE)/graphite (NE) [...] Read more.

We present a time-domain direct current (DC) approach to differentiate positive- (PE) and negative-electrode (NE) contributions from two-electrode full-cell signals in lithium-ion batteries, enabling electrode-resolved diagnostics without specialized instrumentation. The responses of a LiNi_0.8Co_0.1Mn_0.1O₂ (PE)/graphite (NE) cell were recorded across −20 to 20 °C during galvanostatic pulses and subsequent open-circuit relaxations, alongside electrochemical impedance spectroscopy (EIS) measurements. These responses were analyzed using an equivalent-circuit-based model to decompose them into terms with characteristic times. Their distinct temperature dependences enabled attribution of the dominant terms to PE or NE, especially at low temperatures where temporal separation is substantial. The electrode attribution and activation energies were cross-validated against three-electrode measurements and were consistent with EIS-derived time constants. Reconstructing full-cell voltage transients from the identified terms reproduced the measured electrode-specific behavior, and quantitative comparisons showed that the DC time-domain separation aligned closely with directly measured PE/NE overpotentials during the current pulse. These results demonstrate a practical pathway to extract electrode-resolved information from cell voltage alone, offering new methodological possibilities for battery diagnostics and management while complementing three-electrode and alternating current (AC) techniques that are often constrained in field applications. Full article

(This article belongs to the Special Issue Control, Modelling, and Management of Batteries)

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22 pages, 15904 KB

Open AccessArticle

Multi-Timescale Estimation of SOE and SOH for Lithium-Ion Batteries with a Fractional-Order Model and Multi-Innovation Filter Framework

by Jing Yu and Fang Yao

Batteries 2025, 11(10), 372; https://doi.org/10.3390/batteries11100372 - 10 Oct 2025

Cited by 1 | Viewed by 664

Abstract

Based on a fractional-order equivalent circuit model, this paper proposes a multi-timescale collaborative State of Energy (SOE) and State of Health (SOH) estimation method (FOASTFREKF-EKF) for lithium batteries to mitigate the influence of model inaccuracies and battery aging on SOE estimation. Initially, a [...] Read more.

Based on a fractional-order equivalent circuit model, this paper proposes a multi-timescale collaborative State of Energy (SOE) and State of Health (SOH) estimation method (FOASTFREKF-EKF) for lithium batteries to mitigate the influence of model inaccuracies and battery aging on SOE estimation. Initially, a fractional-order equivalent circuit model is built, and its parameters are identified offline using the Starfish Optimization Algorithm (SFOA) to establish a high-fidelity battery model. An H∞ filter is then integrated to improve the algorithm’s resilience to external disturbances. Furthermore, an adaptive noise covariance adjustment mechanism is employed to reduce the effect of operational noise, and a time-varying attenuation factor is introduced to improve the algorithm’s tracking and convergence capabilities during abrupt system-state changes. A joint estimator is subsequently constructed, which uses an Extended Kalman Filter (EKF) for the online determination of battery parameters and SOH assessment. This approach minimizes the effect of varying model parameters on SOE accuracy while reducing computational load through multi-timescale methods. Experimental validation under diverse operating conditions shows that the proposed algorithm achieves root mean square errors (RMSE) of less than 0.21% for SOE and 0.31% for SOH. These findings demonstrate that the method provides high accuracy and reliability under complex operating conditions. Full article

(This article belongs to the Special Issue Control, Modelling, and Management of Batteries)

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15 pages, 508 KB

Open AccessArticle

Demand-Adapting Charging Strategy for Battery-Swapping Stations

by Benjamín Pla, Pau Bares, Andre Aronis and Augusto Perin

Batteries 2025, 11(7), 251; https://doi.org/10.3390/batteries11070251 - 2 Jul 2025

Viewed by 1137

Abstract

This paper analyzes the control strategy for urban battery-swapping stations by optimizing the charging policy based on real-time battery demand and the time required for a full charge. The energy stored in available batteries serves as an electricity buffer, allowing energy to be [...] Read more.

This paper analyzes the control strategy for urban battery-swapping stations by optimizing the charging policy based on real-time battery demand and the time required for a full charge. The energy stored in available batteries serves as an electricity buffer, allowing energy to be drawn from the grid when costs or equivalent

{CO}_{2}

emissions are low. An optimized charging policy is derived using dynamic programming (DP), assuming average battery demand and accounting for both the costs and emissions associated with electricity consumption. The proposed algorithm uses a prediction of the expected traffic in the area as well as the expected cost of electricity on the net. Battery tests were conducted to assess charging time variability, and traffic density measurements were collected in the city of Valencia across multiple days to provide a realistic scenario, while real-time data of the electricity cost is integrated into the control proposal. The results show that incorporating traffic and electricity price forecasts into the control algorithm can reduce electricity costs by up to 11% and decrease associated

{CO}_{2}

emissions by more than 26%. Full article

(This article belongs to the Special Issue Control, Modelling, and Management of Batteries)

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14 pages, 4607 KB

Open AccessArticle

A Novel Capacity Estimation Method for Lithium-Ion Batteries Based on the Adam Algorithm

by Yingying Lian and Dongdong Qiao

Batteries 2025, 11(3), 85; https://doi.org/10.3390/batteries11030085 - 20 Feb 2025

Cited by 1 | Viewed by 1772

Abstract

Accurate estimation of the capacity of lithium-ion batteries is crucial for battery management and secondary utilization, which can ensure the healthy and efficient operation of the battery system. In this paper, we propose multiple machine learning algorithms to estimate the capacity using the [...] Read more.

Accurate estimation of the capacity of lithium-ion batteries is crucial for battery management and secondary utilization, which can ensure the healthy and efficient operation of the battery system. In this paper, we propose multiple machine learning algorithms to estimate the capacity using the incremental capacity (IC) curve features, including the adaptive moment estimation (Adam) model, root mean square propagation (RMSprop) model, and support vector regression (SVR) model. The Kalman filter algorithm is first used to construct the IC curve, and the peak and corresponding voltages correlated with battery life were analyzed and extracted as capacity estimation features. The three models were then used to learn the relationship between aging features and capacity. Finally, the lithium-ion battery cycle aging data were used to validate the capacity estimation performance of the three proposed machine learning models. The results show that the Adam model performs better than the other two models, balancing efficiency and accuracy in the capacity estimation of lithium-ion batteries throughout the entire lifecycle. Full article

(This article belongs to the Special Issue Control, Modelling, and Management of Batteries)

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24 pages, 11173 KB

Open AccessArticle

Advanced State-of-Health Estimation for Lithium-Ion Batteries Using Multi-Feature Fusion and KAN-LSTM Hybrid Model

by Zhao Zhang, Runrun Zhang, Xin Liu, Chaolong Zhang, Gengzhi Sun, Yujie Zhou, Zhong Yang, Xuming Liu, Shi Chen, Xinyu Dong, Pengyu Jiang and Zhexuan Sun

Batteries 2024, 10(12), 433; https://doi.org/10.3390/batteries10120433 - 6 Dec 2024

Cited by 21 | Viewed by 5206

Abstract

Accurate assessment of battery State of Health (SOH) is crucial for the safe and efficient operation of electric vehicles (EVs), which play a significant role in reducing reliance on non-renewable energy sources. This study introduces a novel SOH estimation method combining Kolmogorov–Arnold Networks [...] Read more.

Accurate assessment of battery State of Health (SOH) is crucial for the safe and efficient operation of electric vehicles (EVs), which play a significant role in reducing reliance on non-renewable energy sources. This study introduces a novel SOH estimation method combining Kolmogorov–Arnold Networks (KAN) and Long Short-Term Memory (LSTM) networks. The method is based on fully charged battery characteristics, extracting key parameters such as voltage, temperature, and charging data collected during cycles. Validation was conducted under a temperature range of 10 °C to 30 °C and different charge–discharge current rates. Notably, temperature variations were primarily caused by seasonal changes, enabling the experiments to more realistically simulate the battery’s performance in real-world applications. By enhancing dynamic modeling capabilities and capturing long-term temporal associations, experimental results demonstrate that the method achieves highly accurate SOH estimation under various charging conditions, with low mean absolute error (MAE) and root mean square error (RMSE) values and a coefficient of determination (

R^{2}

) exceeding 97%, significantly improving prediction accuracy and efficiency. Full article

(This article belongs to the Special Issue Control, Modelling, and Management of Batteries)

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20 pages, 4412 KB

Open AccessArticle

Prediction of Lithium-Ion Battery State of Health Using a Deep Hybrid Kernel Extreme Learning Machine Optimized by the Improved Black-Winged Kite Algorithm

by Juncheng Fu, Zhengxiang Song, Jinhao Meng and Chunling Wu

Batteries 2024, 10(11), 398; https://doi.org/10.3390/batteries10110398 - 8 Nov 2024

Cited by 10 | Viewed by 2755

Abstract

The accurate prediction of lithium-ion battery state of health (SOH) can extend battery life, enhance device safety, and ensure sustained reliability in critical applications. Addressing the non-linear and non-stationary characteristics of battery capacity sequences, a novel method for predicting lithium battery SOH is [...] Read more.

The accurate prediction of lithium-ion battery state of health (SOH) can extend battery life, enhance device safety, and ensure sustained reliability in critical applications. Addressing the non-linear and non-stationary characteristics of battery capacity sequences, a novel method for predicting lithium battery SOH is proposed using a deep hybrid kernel extreme learning machine (DHKELM) optimized by the improved black-winged kite algorithm (IBKA). First, to address the limitations of traditional extreme learning machines (ELMs) in capturing non-linear features and their poor generalization ability, the concepts of auto encoders (AEs) and hybrid kernel functions are introduced to enhance ELM, resulting in the establishment of the DHKELM model for SOH prediction. Next, to tackle the challenge of parameter selection for DHKELM, an optimal point set strategy, the Gompertz growth model, and a Levy flight strategy are employed to optimize the parameters of DHKELM using IBKA before model training. Finally, the performance of IBKA-DHKELM is validated using two distinct datasets from NASA and CALCE, comparing it against ELM, DHKELM, and BKA-DHKELM. The results show that IBKA-DHKELM achieves the smallest error, with an RMSE of only 0.0062, demonstrating exceptional non-linear fitting capability, high predictive accuracy, and good robustness. Full article

(This article belongs to the Special Issue Control, Modelling, and Management of Batteries)

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