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August 26, 2025Science and Technology of Engineering Chemistry and Environmental ProtectionOpen Access

TCN method development for SOC prediction of Li-ion batteries

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Key Points

Temporal convolutional networks improve state-of-charge estimation in lithium batteries, enhancing safety and performance.
Adopting strategies like attention mechanisms and genetic algorithms optimizes network structure and hyperparameters.
The review covers recent advancements addressing challenges like data sensitivity and generalization across battery cells.
These findings support better battery management systems, crucial for electric vehicles, especially as batteries age.

Implication

Review highlights TCN methods' effectiveness in SOC estimation for lithium batteries, indicating improved accuracy under various conditions.

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Cite This Study

Zhiyu Chen (2025) studied this question.

synapsesocial.com/papers/68af63e9ad7bf08b1eae4764 https://doi.org/10.61173/r7mzap95

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Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

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