Battery Charging Control Using History-Based Life Extension
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Solution Overview
Problem
Conventional battery charging methods do not optimize battery lifespan as they fail to consider individual battery conditions and histories, leading to varying effects on different batteries, with some methods potentially decreasing lifespan despite attempts to extend it.
Innovation Solution
A method that detects a battery charge request, receives and analyzes multiple battery parameters from the charging history to determine optimized charging parameters, such as current and voltage levels, using similarity measures to match the battery's condition with pre-defined profiles, thereby extending the battery's cycle life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If predetermined charging actions with hard coded thresholds are used, then charging simplicity is maintained, but battery life optimization is compromised
Solution Approach 1:
The charging system dynamically adjusts charging parameters (current, voltage, temperature limits) based on real-time battery conditions and historical data, transitioning from static hard-coded thresholds to adaptive control that optimizes battery life while maintaining operational simplicity
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring battery parameters (temperature, voltage, current) and comparing them against learned optimal values from historical charging patterns, enabling automatic adjustment of charging strategies to extend battery lifespan
2Reliability
If individualized charging parameters based on battery history are implemented, then battery life is optimized, but system complexity increases
Solution Approach 1:
The battery management system performs self-learning by automatically analyzing historical charging data and identifying optimal charging patterns without requiring external intervention or complex user configuration, reducing the perceived system complexity while achieving personalized charging optimization
Solution Approach 2:
The system pre-processes and stores battery historical data in structured formats during normal operation, preparing lookup tables and parameter ranges in advance so that real-time charging decisions can be made quickly without complex calculations during actual charging cycles
3Productivity
If charging parameters are optimized for each battery's unique history, then charging effectiveness is improved, but measurement and detection difficulty increases
Solution Approach 1:
The system creates simplified digital representations (models) of battery state based on historical data, using representative parameters and lookup tables that capture essential battery characteristics without requiring direct measurement of all underlying chemical and physical parameters
Data Source
AI summary
A method includes detecting a request for a battery charge, receiving multiple battery parameters representative of battery charging history, determining a charging parameter to increase battery life as a function of the multiple battery parameters, and charging the battery in accordance with the charging parameter.