Battery Internal Resistance Offset Correction for Aging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing battery monitoring methods fail to accurately predict battery power-delivery performance under varying load conditions and do not account for battery aging, leading to under-utilization and the need for frequent testing.
Innovation Solution
A method that models and dynamically updates internal resistance based on battery operating parameters, using a calibration discharge cycle to maintain prediction accuracy as the battery ages, allowing for user-defined power levels and targets to optimize battery utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If battery status indicators are based on state of charge or voltage, then the battery system provides simple status indication, but it fails to accurately predict power-delivery performance under varying load conditions
Solution Approach 1:
The patent transitions from using simple voltage or state-of-charge parameters to using internal resistance as the key parameter for predicting power-delivery performance. Internal resistance is calculated from measured voltage and current values during discharge cycles, and this parameter directly correlates with the battery's ability to deliver power under varying load conditions.
2Stability of the object's composition
If battery systems rely on a-priori testing and standardized load tests, then the testing process is standardized and reproducible, but the system does not provide dynamic updates to account for battery aging and behavior changes
Solution Approach 1:
The battery management system performs self-calibration by automatically comparing measured internal resistance values against expected values from discharge cycle maps. The system generates correction factors autonomously and uses these to adjust future predictions, eliminating the need for manual re-testing while adapting to battery aging.
Solution Approach 2:
The system continuously monitors actual battery performance during discharge cycles, compares measured internal resistance against predicted values, and uses the difference (correction factor) to refine future predictions. This closed-loop feedback mechanism enables dynamic adaptation to battery aging while maintaining standardized testing procedures.
3Device complexity
If battery systems use fixed discharge cycle maps without correction, then the prediction model is simple to implement, but prediction accuracy deteriorates as the battery ages
Solution Approach 1:
The system pre-generates discharge cycle maps containing expected internal resistance values for various states of charge during discharge. These maps serve as lookup tables that provide quick predictions without complex real-time calculations, maintaining simplicity while enabling accurate predictions when combined with correction factors.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Battery management systems and methods related to predicting power-delivery performance are provided. In one embodiment, a method for predicting power-delivery performance for a battery (102) includes retrieving a plurality of battery operating parameters (106, 108, 110, 112) for a selected discharge cycle, calculating an offset (114) indicative of a difference between a modeled internal resistance (118) of the battery (102) and an observed internal resistance (116) generated from a calibration discharge cycle of the battery prior to the selected discharge cycle, and outputting a battery power-delivery performance prediction (128) based on an offset-corrected internal resistance indicative of a difference between a modeled internal resistance (118) based on the plurality of battery operating parameters (106, 108, 110, 112) for the selected discharge cycle and the offset (114).