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This study presents a novel method for estimating the state of charge (SOC) of lithium-ion batteries under extreme temperatures using an adaptive temporal convolutional network (TCN). The proposed method demonstrated significant improvements in accuracy and generalizability compared to traditional machine learning methods, with average errors decreasing by at least 49.66%. Experiments were conducted on five types of batteries across various temperatures and driving cycles to validate the effectiveness of th

Author
Mustafa Alhumayre
Language
EN

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