Adaptive Battery Capacity Estimation via Dynamic Look-Up Table Correction
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Solution Overview
Problem
Existing methods for estimating battery capacity, such as using look-up tables based on voltage-capacity characteristic curves, face challenges due to battery degradation and the difference between charge-mode and discharge-mode curves, leading to inaccurate capacity estimation, especially for LiFePO4 batteries with flat slope sections and transition states.
Innovation Solution
A method that initializes the battery to a specific state, discharges it by a controlled amount, measures the open-circuit voltage, and corrects the look-up table based on the difference between actual and look-up capacities to improve estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a look-up table based on voltage-capacity characteristic curves is used to estimate battery capacity, then the estimation process is simple and fast, but the accuracy deteriorates due to battery degradation and differences between charge-mode and discharge-mode curves
Solution Approach 1:
The patent applies dynamics by making the look-up table adaptive and updatable. Instead of using a static look-up table, the system dynamically updates the table with actual measured capacity data from the battery over time. This allows the estimation system to adapt to battery degradation and changing conditions, resolving the contradiction between simple/fast estimation and accurate results.
Solution Approach 2:
The patent implements feedback by using actual capacity measurements from controlled discharge tests to correct and update the look-up table. The system performs discharge tests, compares actual capacity with look-up table values, and uses this feedback to update the table entries. This closed-loop feedback mechanism improves accuracy while maintaining the simplicity of look-up table-based estimation.
2Measurement precision
If the look-up table is updated frequently with actual capacity measurements, then the estimation accuracy improves, but the time and resource consumption increases
Solution Approach 1:
The patent applies periodic action by scheduling capacity estimation and look-up table updates at specific intervals or under specific conditions rather than continuously. The system performs discharge tests and updates the look-up table periodically or when triggered by certain events (e.g., after a threshold number of cycles, when capacity deviation exceeds a threshold). This reduces time and resource consumption while maintaining improved accuracy.
Solution Approach 2:
The patent uses parameter changes by adjusting the update frequency and thresholds based on battery state and usage patterns. The system can change parameters such as the minimum capacity deviation threshold for triggering an update, or the time interval between updates, optimizing the balance between accuracy improvement and time/resource consumption for different operating conditions.
Data Source
AI summary
A method for estimating a capacity of a battery includes providing a look-up table storing data relating to a voltage and a capacity of the battery, and initializing the battery to reach an initialization state which serves as a starting point for a discharging process. The method also includes discharging the battery, from the initialization state, by a first amount of charge to reach a first state, calculating an actual capacity of the battery based on a measured amount of charge discharged from the initialization state to the first state, measuring an open-circuit voltage at the first state, obtaining a look-up capacity of the battery from the look-up table according to the open-circuit voltage measured at the first state, calculating a difference between the actual capacity and the look-up capacity, and correcting the look-up table based on the difference.