Optimizing Battery Life in iOS Apps with AI-Driven Energy Management
Darsh Viroja
, Cyril Thomas , Piyush Kashiyani , Khyati Joshi
Abstract - Battery life remains a critical challenge in mobile application development, especially for iOS platforms, where users expect both performance and longevity. Traditional battery optimization techniques are static and struggle to adapt to dynamic usage patterns. This paper introduces an intelligent, AI-driven framework that leverages Core ML to dynamically monitor, predict, and manage battery-consuming tasks in real time. By analyzing user behavior and system-level metrics, the proposed system adjusts background task execution, rendering behavior, and GPS activity to reduce energy consumption. Our results demonstrate up to 40% improvement in battery performance across various use cases, proving the system's effectiveness and adaptability.
Index Terms - iOS, Battery Optimization, Core ML, AI, SwiftUI, Mobile Applications, Energy Management
"Optimizing Battery Life in iOS Apps with AI-Driven Energy Management", TIJER - TIJER - INTERNATIONAL RESEARCH JOURNAL (www.TIJER.org), ISSN:2349-9249, Vol.12, Issue 4, page no.a178-a180, April-2025, Available :https://tijer.org/TIJER/papers/TIJER2504028.pdf
Volume 12
Issue 4,
April-2025
Pages : a178-a180
Paper Reg. ID: TIJER_157326
Published Paper Id: TIJER2504028
Downloads: 000293
Research Area: Science and Technology
Country: Rajkot , Gujarat, India
ISSN: 2349-9249 | IMPACT FACTOR: 8.57 Calculated By Google Scholar | ESTD YEAR: 2014
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: TIJER (IJ Publication) Janvi Wave
