Research on the heat dissipation performance of new energy batteries in intelligent connected vehicles based on deep learning

Nannan Wang

doi:10.1117/12.3026546

15 March 2024 Research on the heat dissipation performance of new energy batteries in intelligent connected vehicles based on deep learning

Nannan Wang

Author Affiliations +

Proceedings Volume 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023); 1307517 (2024) https://doi.org/10.1117/12.3026546
Event: Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 2023, Kunming, China

Abstract

Deep Learning (DL) can automatically extract useful features from massive data and conduct efficient pattern recognition. This technology provides a new method and perspective for solving many complex problems. Whether it is intelligent perception, decision control or fault diagnosis, DL has shown a broad application prospect. In this article, DL technology is combined with the research on the heat dissipation performance of intelligent networked automobile new energy battery. By constructing and training DL model, the heat dissipation performance of the battery is predicted and optimized, and the prediction results of the model are analyzed and discussed. By comparing the prediction accuracy, error and response time of different algorithms, the results show that the prediction accuracy of this algorithm is improved by 26.64%, the prediction error is reduced by more than 30%, and the response time is also obviously reduced. These results prove the effectiveness of the proposed algorithm in predicting the heat dissipation performance of new energy batteries. The optimization algorithm is of great significance for improving the energy utilization efficiency of intelligent networked vehicles, enhancing system stability, realizing intelligent energy management and promoting the development of autonomous driving technology of new energy batteries.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Nannan Wang "Research on the heat dissipation performance of new energy batteries in intelligent connected vehicles based on deep learning", Proc. SPIE 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 1307517 (15 March 2024); https://doi.org/10.1117/12.3026546

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KEYWORDS

Batteries

Data modeling

Performance modeling

Mathematical optimization

Deep learning

Feature extraction

Neural networks

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