The Role of AI in Optimizing Power Consumption in Mobile Devices: A Comprehensive Review

Abstract

The rapid advancement of mobile devices has significantly increased power consumption, presenting major challenges to energy efficiency and sustainability. As mobile devices become central to daily life, optimizing power management has emerged as a critical research priority. This paper provides a comprehensive narrative review of Artificial Intelligence (AI) techniques, including Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL), for optimizing power consumption in mobile devices. A narrative review methodology was employed, searching IEEE Xplore, ACM Digital Library, ScienceDirect, and Google Scholar databases using keywords including "AI power optimization," "machine learning battery management," and "mobile device energy efficiency." Studies published between 2020 and 2025 were included, focusing on peer-reviewed articles addressing AI-based power management in mobile computing environments. The analysis reveals that ML approaches demonstrate improvements in battery life prediction accuracy, while DL techniques excel at modeling complex, non-linear power consumption patterns. RL methods show particular promise for real-time adaptive power management, with studies reporting battery life improvements ranging from 12% to 27% compared to traditional approaches. However, significant challenges remain, including data requirements, real-time optimization constraints, hardware limitations, and scalability across diverse device platforms. AI-driven power management represents a promising frontier for mobile energy optimization. Future research directions include the integration of federated learning for privacy-preserving optimization, quantum computing for complex optimization problems, IoT-based coordinated power management, and the development of cross-platform AI models. Additionally, research should focus on developing lightweight models suitable for resource-constrained devices, creating standardized benchmarks for evaluating AI power management solutions, and exploring edge-cloud hybrid architectures to balance computational demands with energy efficiency.