Studying Abuse Testing on Lithium-Ion Battery Packaging for Energy Storage Systems

In order to cope with the failure of existing fault analysis schemes for AC/DC distribution networks with a high proportion of distributed generations, this paper proposes a fault characteristic analysis method for AC/DC distribution networks that considers the influence of distributed generation control strategies. Firstly, a transient model for the AC/DC distribution network connected to distributed generations is built. Then, the fault characteristics of the AC/DC distribution network in different stages, such as the capacitor discharge stage, inductive renewal stage, and steady state stage, is analyzed. Finally, detailed simulation analysis is conducted using PSCAD/EMTDC to validate the effectiveness of the developed scheme by the superior approximation performance between simulated curves and calculated curves.

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A Temporal Fusion Memory Network-Based Method for State-of-Health Estimation of Lithium-Ion Batteries

Chen,

Wang,

Guo

2024

Batteries

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As energy storage technologies and electric vehicles evolve quickly, it becomes increasingly difficult to precisely gauge the condition (SOH) of lithium-ion batteries (LiBs) during rapid charging scenarios. This paper introduces a novel Time-Fused Memory Network (TFMN) for SOH estimation, integrating advanced feature extraction and learning techniques. Both directly measured and computationally derived features are extracted from the charge/discharge curves to simulate real-world fast-charging conditions. This comprehensive process captures the complex dynamics of battery behavior effectively. The TFMN method utilizes one-dimensional convolutional neural networks (1DCNNs) to capture local features, refined further by a channel self-attention module (CSAM) for robust SOH prediction. Long short-term memory (LSTM) modules process these features to capture long-term dependencies essential for understanding evolving battery health patterns. A multi-head attention module enhances the model by learning varied feature representations, significantly improving SOH estimation accuracy. Validated on a self-constructed dataset and the public Toyota dataset, the model demonstrates superior accuracy and robustness, improving performance by 30–50% compared to other models. This approach not only refines SOH estimation under fast-charging conditions but also offers new insights for effective battery management and maintenance, advancing battery health monitoring technologies.

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Studying Abuse Testing on Lithium-Ion Battery Packaging for Energy Storage Systems

Cited by 3 publications

References 35 publications

The Influence of Overcharging and Over-Discharging on the Capacity Degradation of Lithium-Ion Batteries

The Influence of Overcharging and Over-Discharging on the Capacity Degradation of Lithium-Ion Batteries

Fault Characterization for AC/DC Distribution Networks Considering the Control Strategy of Photovoltaic and Energy Storage Battery

A Temporal Fusion Memory Network-Based Method for State-of-Health Estimation of Lithium-Ion Batteries

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