Model-independent Battery Life Forecaster
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Solution Overview
Problem
Existing methods for estimating battery life and performance of rechargeable batteries are time-consuming and resource-intensive, relying on statistical models that require extensive testing and may not accurately reflect real-world usage, leading to inaccuracies due to performance variance and limited representation of actual battery conditions.
Innovation Solution
A model-independent battery forecaster that estimates battery parameters such as remaining capacity, internal resistance, and reversible potential based on real-time measurements of current, voltage, and temperature during effective power cycles, eliminating the need for type-specific statistical models and allowing for estimation of battery life and performance trends without comparison to pre-existing models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical models are generated through extensive testing of multiple rechargeable batteries, then estimation accuracy for average battery performance is improved, but resource consumption and time costs increase significantly
Solution Approach 1:
The patent extracts the essential estimation logic from complex statistical models by identifying and using only the critical parameters (voltage, current, temperature) that directly correlate with battery state. This eliminates the need for extensive testing and model generation while retaining estimation capability through direct physical measurements rather than statistical correlations.
Solution Approach 2:
Instead of creating statistical models through testing multiple batteries, the patent uses a single battery's actual operational data as the reference. The estimation algorithm copies and analyzes the real-time behavior patterns of the specific battery being monitored, eliminating the need to generalize from population statistics to individual cases.
2Reliability
If statistical models based on full charge and discharge operations are used, then a comprehensive battery profile is obtained, but the estimates do not accurately reflect real-world usage conditions
Solution Approach 1:
The patent transitions from static full charge-discharge cycle modeling to dynamic real-time monitoring. The estimation algorithm continuously adapts to the battery's actual operational state, adjusting to partial charges, variable loads, and intermittent usage patterns that characterize real-world applications rather than standardized test cycles.
Solution Approach 2:
The patent changes the operational parameters from standardized full charge-discharge cycles to actual variable usage conditions. By monitoring voltage, current, and temperature during real operational patterns including partial charges and varying discharge rates, the system captures authentic battery behavior under diverse loading conditions rather than idealized test scenarios.
3Measurement precision
If type-specific statistical models are generated for different rechargeable battery types, then accuracy for each specific type is improved, but device complexity and resource requirements increase
Solution Approach 1:
The patent develops a universal estimation algorithm that functions across different battery types without requiring type-specific statistical models. The core methodology of monitoring voltage, current, and temperature relationships remains the same regardless of battery chemistry or capacity, eliminating the need to generate and maintain separate models for each battery type while maintaining estimation accuracy.
Solution Approach 2:
The patent uses parameter normalization and scaling techniques that allow the same estimation algorithm to adapt to different battery types through parameter adjustment rather than model regeneration. By expressing battery state in terms of normalized voltage, current, and temperature relationships, the system achieves type-agnostic estimation that automatically adapts to different battery characteristics.
Data Source
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AI summary
A method includes determining parameters of a rechargeable battery during an effective power cycle of the rechargeable battery. The parameters include a current provided by the rechargeable battery, a voltage across the rechargeable battery, and a temperature of the rechargeable battery. The effective power cycle includes multiple charge operations and multiple discharge operations. The method includes estimating a remaining capacity of the rechargeable battery based on the current, the voltage, and the temperature. The method also includes generating an output indicating the remaining capacity.