A Multi-Scenario Smartphone Battery Life Optimization Model Based on the Entropy Weight Method, Grey Relational Analysis, and Linear Programming

To mitigate the bottleneck of insufficient smartphone battery life in high-frequency usage scenarios and balance endurance performance with user experience, this study develops a multi-scenario battery life optimization model. A hybrid weighting model combining the Entropy Weight Method (EWM) and Grey Relational Analysis (GRA) is adopted to identify core influencing factors with 98.5% accuracy, with network type and screen brightness confirmed as the dominant ones. Scenario-specific linear programming models are constructed for gaming, daily use and navigation, with scenario-based constraints incorporated to maximize battery life. The model achieves an average 18.7% improvement in battery life, with respective gains of 22.3%, 18.7% and 15.2% for the three scenarios. A three-dimensional validation framework of effectiveness, stability and robustness verifies that all parameter and battery life ratio deviations are within 10%. Compared with traditional models, the proposed model features 35% higher computational efficiency and superior scenario adaptability, providing a practical theoretical reference for the design of intelligent battery management systems for smartphones.