Battery Charge Profile Personalization for Real-Time Adaptive Charging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing battery management systems provide static charge profiles that do not account for user needs or battery health, leading to reduced user experience and degraded battery performance, and require additional circuitry and hardware for adaptive charging, which is not capable of generating custom real-time profiles.
Innovation Solution
An on-device method for real-time customization of battery charge profiles using machine learning models to determine charging and discharging behavior parameters, incorporating user characteristics and battery health, to generate personalized charging profiles that adapt to usage patterns and conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static charge profiles are used in existing battery management systems, then device complexity is reduced, but battery performance and user experience deteriorate due to inability to adapt to user needs and battery health status
Solution Approach 1:
The battery management system performs self-learning by automatically monitoring charging patterns, battery health status, and usage behaviors without requiring external intervention. The system autonomously generates personalized charge profiles based on accumulated data, eliminating the need for complex manual configuration or additional hardware while adapting to user needs over time.
Solution Approach 2:
The system dynamically adjusts charging parameters such as charging current, voltage, and temperature thresholds based on real-time battery health status and learned user preferences. By changing operational parameters adaptively rather than using fixed static profiles, the system achieves high adaptability without requiring complex hardware modifications.
2Reliability
If existing adaptive charge profile methods are implemented, then battery performance can be improved, but device complexity increases due to requirement of extra circuitry and hardware elements
Solution Approach 1:
The patent replaces physical hardware-based adaptive charging systems with a software/firmware-based machine learning solution. Instead of using extra circuitry and hardware elements to achieve adaptive charging, the system uses computational algorithms running on existing device processors to analyze charging patterns and generate optimized charge profiles, thereby maintaining battery performance while avoiding increased hardware complexity.
Solution Approach 2:
The machine learning model is implemented using existing multi-functional components in modern electronic devices (processors, memory, sensors). The same hardware resources that perform general device functions are leveraged to execute the adaptive charging algorithm, eliminating the need for dedicated adaptive charging hardware and reducing overall device complexity.
3Reliability
If fixed charge profiles are used across different operating conditions, then device complexity is minimized, but battery performance deteriorates due to temperature throttling and varying usage conditions
Solution Approach 1:
The charging profile transitions from a static fixed pattern to a dynamic adaptive pattern that continuously adjusts based on operating conditions. The machine learning model monitors temperature, charging speed, and usage patterns in real-time, dynamically modifying charging parameters to optimize battery performance under varying conditions without requiring complex manual control systems.
Solution Approach 2:
The system implements closed-loop feedback by continuously monitoring battery temperature, charging status, and usage patterns, then using this feedback to adjust subsequent charging behavior. The machine learning model learns from past charging cycles and automatically optimizes charging parameters for different operating conditions, achieving adaptive performance through software-based feedback control rather than complex hardware systems.
Data Source
AI summary
A method for on-device real-time customization of charge profiles for a battery in an electronic device, and/or a corresponding device. The method may include determining at least one charging behavior parameter of the battery during every charging cycle. Further, the method may include determining at least one discharging behavior parameter of the battery subsequent to every charging cycle. Further, the method may include generating a charging profile for charging the battery based on the at least one charging behavior parameter and the at least one discharging behavior parameter. Further, the method may include charging the battery using the generated charging profile for the subsequent charging-discharging cycles.