Research on optimal application of vehicle edge computing in intelligent networked vehicle battery management

Nannan Wang

doi:10.1117/12.3026557

15 March 2024 Research on optimal application of vehicle edge computing in intelligent networked vehicle battery management

Nannan Wang

Author Affiliations +

Proceedings Volume 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023); 130751B (2024) https://doi.org/10.1117/12.3026557
Event: Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 2023, Kunming, China

Abstract

With the continuous development of Intelligent Networked Vehicle (INV), electric vehicles have become one of the important trends in the automobile industry. In INV, battery management system is very important to ensure battery performance and life. In this study, the optimal application of Edge Computing (EC) in INV battery management is discussed, aiming at improving the real-time performance, energy efficiency and user experience of the system. Through data processing and decision-making in the vehicle, EC effectively reduces the dependence on the central cloud server, reduces the data transmission delay and improves the real-time performance of the system. A comprehensive mathematical model is established, which considers the dynamic characteristics, charging and discharging process and multiple optimization objectives of the battery. In order to solve this optimization problem, this paper adopts Adaptive Genetic Algorithm (AGA) to find the optimal solution in complex and nonlinear problems. Through the solution of the model, the feasible battery management strategies in the actual scene are obtained, which can balance the needs of users, maximize battery performance and prolong battery life. The research results show that with the support of vehicle EC, the battery management system can respond to different driving scenarios more intelligently, improve energy efficiency, prolong battery life and provide a better user experience. The conclusion is of positive significance for promoting the further development of INV technology and the continuous improvement of intelligent transportation system.

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

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Nannan Wang "Research on optimal application of vehicle edge computing in intelligent networked vehicle battery management", Proc. SPIE 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 130751B (15 March 2024); https://doi.org/10.1117/12.3026557

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KEYWORDS

Batteries

Mathematical optimization

Data processing

Decision making

Mathematical modeling

Intelligence systems

Clouds

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