State-of-Health Prediction of Lithium-Ion Batteries Using Feature Fusion and a Hybrid Neural Network Model

Energy, Год журнала: 2025, Номер unknown, С. 135163 - 135163

Опубликована: Фев. 1, 2025

Язык: Английский

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State of health estimation for lithium-ion batteries based on optimal feature subset algorithm

Energy, Год журнала: 2025, Номер unknown, С. 135685 - 135685

Опубликована: Март 1, 2025

Язык: Английский

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A Joint Estimation Method for the SOC and SOH of Lithium-Ion Batteries Based on AR-ECM and Data-Driven Model Fusion

Electronics, Год журнала: 2025, Номер 14(7), С. 1290 - 1290

Опубликована: Март 25, 2025

Accurate estimations of State-of-Charge (SOC) and State-of-Health (SOH) are crucial for ensuring the safe efficient operation lithium-ion batteries in Battery Management Systems (BMSs). This paper proposes a novel joint estimation method integrating an Autoregressive Equivalent Circuit Model (AR-ECM) with data-driven model to address strong coupling between SOC SOH. First, multi-strategy improved Ivy algorithm (MSIVY) is utilized optimize hyperparameters Hybrid Kernel Extreme Learning Machine (HKELM). Key voltage interval features, including split voltage, differential capacity, current–voltage product, extracted filtered using sliding window approach enhance SOH prediction accuracy. The estimated subsequently incorporated into AR-ECM state-space equations, where enhanced particle swarm optimization optimizes parameters. Finally, Extended Kalman Filter (EKF) applied achieve collaborative SOC–SOH estimation. Experimental results demonstrate that proposed achieves errors below 1% under 2% on public datasets, showcasing its robust generalization capability real-time performance.

Язык: Английский

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Online Remaining Useful Life Prediction of Lithium-ion Batteries Based on Hybrid Model

Journal of The Electrochemical Society, Год журнала: 2025, Номер 172(4), С. 040503 - 040503

Опубликована: Апрель 1, 2025

A hybrid model based on black-winged kite algorithm and dual-attention mechanism optimized temporal convolutional network (TCN) with simple recurrent unit (SRU) is proposed to improve the accuracy of online remaining-useful-life (RUL) prediction for Li-ion batteries (LIBs). Health indicators (HIs) correlated battery capacity are extracted from calculated variables verified Spearman correlation coefficient constructed, applying TCN multi-head self-attention capture in spatial dimension decay pattern HIs, introducing attention ability SRU timing patterns input sequences as well BKA further optimize hyper-parameters, enhancing performance. Experimental data used validate model’s predictive performance LIBs at different usage levels under complex conditions such regeneration, sharp fluctuations, plunges. The results achieve MAE less than 3.66%, MAPE below 2.02%, RMSE not exceeding 5.03%, R 2 greater 0.96, absolute error RUL 5. experimental demonstrate that can accurate perform good robustness.

Язык: Английский

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Application of Intelligent Low‐Cost Accelerometers for Bridge Monitoring With a Deep Learning Approach

Structural Control and Health Monitoring, Год журнала: 2025, Номер 2025(1)

Опубликована: Янв. 1, 2025

Despite the crucial role of structural health monitoring (SHM) in ensuring integrity and safety essential infrastructure, its adoption is often limited by high costs traditional sensors. This study introduces an innovative approach for creating intelligent, high‐performing low‐cost accelerometers using a deep learning framework rooted long short–term memory (LSTM) neural networks. Initially, commercial sensors are temporarily installed alongside on bridge to facilitate training process. Once complete, removed, leaving calibrated permanently place perform continuous SHM tasks. In case study, was equipped with array six The efficacy this corroborated through comparative analysis mode shapes eigenfrequencies derived from both sensors, as well intelligent accelerometers.

Язык: Английский

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State of Health Estimation for Lithium-Ion Batteries Based on Impedance Feature Selection and Improved Support Vector Regression

Energy, Год журнала: 2025, Номер unknown, С. 136135 - 136135

Опубликована: Апрель 1, 2025

Язык: Английский

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