LITHIUM BATTERY LIFESPAN PREDICTION METHOD INTEGRATING DYNAMIC CONVOLUTION TRANSFORMER AND CMA-ES

Wang Xiongran; Zhang Jing

doi:10.19912/j.0254-0096.tynxb.2024-2097

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (6) : 1-8. DOI: 10.19912/j.0254-0096.tynxb.2024-2097

Special Topics of Academic Papers at the 27th Annual Meeting of the China Association for Science and Technology

LITHIUM BATTERY LIFESPAN PREDICTION METHOD INTEGRATING DYNAMIC CONVOLUTION TRANSFORMER AND CMA-ES

Wang Xiongran, Zhang Jing

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Abstract

The prediction of lithium battery lifespan is of great significance for energy management and maintenance. To address challenges such as complex multidimensional time series data, long-term dependencies, and dynamic changes in characteristics during the prediction process, this paper proposes a lithium-ion battery life prediction model based on dynamic convolutional neural networks and Transformer（DCF）, covariance matrix adaptive adjustment evolution strategy（CMA-ES）, and multi-head self-attention mechanism. The DCF dynamically extracts key features from time series data, reduces data dimensionality and redundancy, and captures long-term dependencies. CMA-ES optimizes model hyperparameters to enhance the model's ability to model local features and global dependencies. The multi-head self-attention mechanism further focuses on important features and handles complex nonlinear dynamic relationships. Experimental validation is conducted using the publicly available lithium battery dataset provided by NASA. Results show that the proposed method achieves a minimum average absolute error of 0.28%, outperforming most existing methods using the same dataset. The experiments further demonstrate improvements in prediction accuracy and generalization ability, especially in long-term lifespan prediction, where the model exhibits higher precision and robustness, providing more reliable technical support for lithium battery lifespan prediction.

Key words

lithium batteries / convolutional neural networks / covariance matrix / multi head self attention mechanism / model hyperparameters / nonlinear

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Wang Xiongran, Zhang Jing. LITHIUM BATTERY LIFESPAN PREDICTION METHOD INTEGRATING DYNAMIC CONVOLUTION TRANSFORMER AND CMA-ES[J]. Acta Energiae Solaris Sinica. 2025, 46(6): 1-8 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2097

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