Remote Battery Policy Adaptation for User Behavior
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Solution Overview
Problem
Existing information handling systems (IHSs) lack efficient battery management policies that adapt to local user behavior, leading to potential battery wear and inefficient energy usage.
Innovation Solution
Implementing a system and method for remotely applying battery management policies based on local user behavior, where an IHS receives and applies policies from a remote server, adjusting charging modes and voltages based on user behavior, including standard, express, primarily AC, adaptive, and custom modes, and autonomously updating policies without continuous server connection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of stationary object
If battery management policies are fixed and not adaptive, then device complexity is reduced, but battery life and energy efficiency deteriorate due to inability to adapt to user behavior
Solution Approach 1:
The battery management policy transitions from a static, fixed configuration to a dynamic system that automatically adapts to user behavior patterns. The system continuously monitors telemetry data, detects behavior changes, and adjusts charging parameters in real-time, enabling the policy to evolve with user needs while extending battery life without significant complexity increases
Solution Approach 2:
The system implements a closed-loop feedback mechanism where telemetry data from battery operations is continuously collected, analyzed, and used to adjust management policies. This feedback-driven approach enables the system to learn from user behavior patterns and optimize charging strategies, resolving the contradiction between adaptability and complexity through intelligent automation
2Productivity
If maximum charging voltage or current is increased to reduce charging time, then productivity is improved, but battery wear increases leading to reduced reliability
Solution Approach 1:
The charging parameters (voltage and current) are dynamically adjusted based on real-time monitoring of battery state and user behavior patterns. The system can switch between aggressive charging modes (higher voltage/current) when battery health permits and conservative modes when wear risk increases, optimizing the trade-off between charging speed and battery durability through continuous adaptation
Solution Approach 2:
The system changes operational parameters (charging voltage and current levels) based on detected user behavior patterns and battery condition. By modulating these parameters dynamically rather than maintaining fixed high values, the system achieves fast charging when needed while preventing excessive wear, thus resolving the contradiction between productivity and reliability
3Adaptability or versatility
If battery management policies are customized for individual users, then adaptability is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system implements self-service through autonomous behavior detection and policy selection. Instead of requiring manual user configuration or complex centralized management, the system automatically monitors its own operation, detects user behavior patterns, and selects appropriate management policies independently, reducing the perceived complexity for users while maintaining high adaptability
Solution Approach 2:
The system creates simplified behavioral profiles by copying and analyzing telemetry data patterns rather than managing complex individual policies for each user. This approach allows the system to adapt to user behavior through pattern recognition and template matching, reducing computational complexity while maintaining customization and adaptability
Data Source
AI summary
Systems and methods for remotely applying battery management policies based on local user behavior. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include: a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: receive a battery management policy from a remote server; and apply the battery management policy to the IHS, wherein the battery management policy is selected based upon a local user's behavior.