Correlation Between AI-Driven Predictive Analytics and Battery Lifespan in Traditional and AI-Operated BMS

Abstract

This paper focuses on understanding how these advancements in AI-based predictive analytics are impacting battery life in both the conventional system and the intelligent battery system. The comparative analysis of these two reveals that the AI-backed BMS is considered to be more effective regarding the battery lifespan and better tolerance to temperature fluctuations. The type of ML used in this paper, particularly the Decision Tree has very high accuracy ratings for the classification of BMSs hence endorsing the use of AI in boosting battery performance. According to the outcomes, implementing the intelligent artificial BMS could pull off an enormous rise in battery lifecycle and density, which makes them extremely beneficial in any field utilizing this technology.