Citation :

Vishwanadham Mandala, 2023. "Integrating AI for Enhanced Battery Lifespan and Efficiency in Electric Vehicles" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 1, Issue 1: 28-36.

Abstract :

Advancements in a variety of artificial intelligence fields have spurred technological advancements not only in power consumption and efficiency but also in battery development, influencing electric vehicle advancement. By recognizing the great importance of batteries in electric vehicles, a platform that unites genetic algorithms with finite element analysis with neural networks in AI has been developed to consider various factors that affect battery design. The results of a series of tests to validate the approach were promising and seemed to be in good agreement with experimental measurements. It was evident that with the help of AI, the current trajectory of already rapid advancements in the EV field is likely to continue, which can be expected to lead to even more efficient, cheaper, and safer electric vehicles with longer lifespans, thereby further reducing the impact on the environment and simultaneously providing global economic and social benefits. Discussions suggest AI use is a must-have factor that needs to evolve under practically unlimited performance data for EVs. In summary, electric vehicles rely heavily on power storage to guarantee long driving range, lightweight, low cost, and long lifespan, with these factors having a complex interrelationship and requiring system optimization. This paper addresses this issue by presenting how integrating AI has the potential to help accelerate EV advancements. The combination of GA/AN with FEA methods studied in this work enables us to optimize our battery systems for different magnet manufacturers, and reduce the unnecessary research and development costs, impairing our battery testing.

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Keywords :

Enhanced Battery Lifespan and Efficiency in Electric Vehicles, Industry 4.0, Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Smart Manufacturing (SM), Computer Science, Data Science,Vehicle, Vehicle Reliability.