Battery Life Estimation Using Partial Cycle Degradation Models
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Solution Overview
Problem
Existing methods fail to accurately estimate the remaining useful life of batteries, particularly in electric vehicles, leading to potential safety issues and high replacement costs due to the gradual reduction in battery capacity over charge cycles.
Innovation Solution
A method and apparatus that estimate battery life by comparing first and second status information using a partial cycle model, adjusting the model as needed, and incorporating user history information to predict future capacity and internal resistance, thereby calculating the remaining useful life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If a battery is used repeatedly over many charge cycles, then the operational duration of the electronic device is extended initially, but the battery capacity gradually reduces leading to shortened battery life and eventual failure
Solution Approach 1:
The system performs preliminary estimation of battery remaining useful life (RUL) by analyzing degradation patterns from previous charge cycles. This allows predicting battery failure before it occurs, enabling proactive replacement decisions that prevent operational failures and extend reliable service life.
Solution Approach 2:
The system continuously monitors battery status information from charge cycles and feeds this data back to update degradation patterns. This feedback loop enables dynamic adjustment of RUL estimates, improving prediction accuracy over time and allowing optimal replacement timing that balances reliability with cost.
2Loss of energy
If battery replacement is delayed to extend usage, then short-term costs are reduced, but the risk of sudden battery failure increases causing safety issues and higher long-term costs
Solution Approach 1:
The system performs preliminary RUL estimation to predict when battery replacement should occur. By providing advance notice of expected battery failure, users can plan replacement during convenient periods with lower costs, avoiding emergency replacements driven by sudden failures.
Solution Approach 2:
The system cushions against the harmful effect of sudden battery failure by continuously monitoring degradation and providing early warning. This allows users to replace batteries before critical failure occurs, preventing safety issues and avoiding expensive emergency replacement scenarios.
3Device complexity
If traditional battery life estimation methods are used, then the system is simple to implement, but the estimation accuracy is insufficient leading to premature or delayed replacement decisions
Solution Approach 1:
The system segments battery life estimation into distinct phases: collecting status information from charge cycles, analyzing degradation patterns from historical data, and calculating RUL based on comparative analysis. This segmentation enables accurate estimation while maintaining manageable system complexity through modular processing.
Solution Approach 2:
The system performs preliminary analysis of degradation patterns from previous charge cycles before making replacement decisions. By pre-processing historical data to establish degradation characteristics, the system achieves high estimation accuracy without requiring complex real-time analysis, thus balancing precision with computational simplicity.
Data Source
AI summary
A method and apparatus for estimating battery life are provided. A method of estimating battery life may involve estimating first status information of a battery based on battery information acquired from the battery, estimating second status information of the battery using a partial cycle model corresponding to a battery degradation pattern for a partial cycle, and calculating the battery life based on a comparison between the first status information and the second status information.