Battery Degradation Estimation Using SOC Extreme-Value Extraction
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Solution Overview
Problem
Existing state estimation methods for battery degradation require complex configurations and lack accuracy in estimating state changes such as degradation speed and amount, necessitating simpler and more precise estimation techniques.
Innovation Solution
A method involving the extraction of local maximum and minimum values from time series data of battery state-of-charge (SOC) to calculate state change speed and amount using relationship data, incorporating a processing circuit to analyze current, voltage, and temperature data to estimate battery degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex estimation methods are used to improve battery degradation estimation accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent extracts and utilizes only the essential extreme value information (local maximum and minimum values) from the time series data, discarding redundant data points. This extraction approach maintains high estimation accuracy by focusing on critical degradation indicators while significantly simplifying the apparatus configuration and data processing requirements.
Solution Approach 2:
The patent segments the continuous time series data into discrete extreme value events (local maxima and minima), transforming a complex continuous signal into manageable discrete events. This segmentation enables simpler processing while preserving the essential degradation information contained in the original continuous data.
2Measurement precision
If comprehensive time series data analysis is performed to improve state change detection, then measurement precision improves, but loss of time increases
Solution Approach 1:
The patent extracts only the extreme value points (local maxima and minima) from the complete time series data, eliminating the need to process every data point. This selective extraction maintains accurate state change detection by focusing on the most informative moments while dramatically reducing data processing time.
Solution Approach 2:
The patent skips over non-extreme data points in the time series, rushing through the data to identify only the critical extreme value events. This approach enables rapid processing while maintaining detection accuracy by bypassing redundant information and focusing directly on significant state change indicators.
Data Source
AI summary
In a state estimation method of an embodiment, from time series data representing a time change of a main physical quantity concerning an estimation target, a local maximum and minimum values of the main physical quantity, and an inter-extreme-value time from an extreme value to the next extreme value are extracted. In the state estimation method, a state change speed of the estimation target is calculated based on relationship data representing the relationship of the state change speed with the local maximum and minimum values and the extracted local maximum and minimum values. In the state estimation method, a state change amount of the estimation target is calculated for each inter-extreme-value time based on the extracted inter-extreme-value time and the calculated state change speed.