Battery Shape Change Estimation from SOC Fluctuation Patterns
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Solution Overview
Problem
Conventional methods for estimating the shape change and deterioration of energy storage devices, such as batteries, face challenges in accuracy, particularly when the state of charge (SOC) fluctuation patterns are complex, and often require stopping battery use for measurements, limiting real-time estimation capabilities.
Innovation Solution
An estimation device and method that acquires time-series data of SOC, determines fluctuation ranges and representative values, and estimates shape change or deterioration based on these parameters, considering the SOC use range and temperature, allowing for accurate predictions even during battery use and complex fluctuation patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional measurement methods are used to estimate thickness increase, then measurement accuracy can be improved, but the battery use must be stopped which reduces productivity
Solution Approach 1:
The patent replaces physical measurement methods (mechanical thickness measurement) with an estimation system that uses electrical parameters (SOC time-series data) to calculate thickness increase. This substitution allows continuous monitoring during battery operation without stopping use, resolving the contradiction between measurement accuracy and productivity.
Solution Approach 2:
The patent introduces an estimation device as an intermediary that processes SOC time-series data to derive thickness increase information. This intermediary system enables indirect measurement of thickness change through electrical characteristics, allowing continuous monitoring without physical intervention that would stop battery operation.
2Productivity
If simple estimation methods are used, then productivity is maintained, but estimation accuracy deteriorates
Solution Approach 1:
The patent performs preliminary calculations by storing multiple candidate thickness increase values corresponding to different SOC fluctuation patterns. During real-time operation, the system selects from pre-calculated values based on current SOC data, enabling accurate real-time estimation without complex on-the-fly calculations that would compromise speed.
Solution Approach 2:
The patent changes the estimation approach from direct physical measurement to parameter-based calculation using SOC time-series data. By monitoring changes in SOC over time and comparing against reference patterns, the system achieves accurate deterioration estimation while maintaining real-time capability and productivity.
3Measurement precision
If detailed SOC analysis is performed to improve estimation accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the essential feature from SOC time-series data - the fluctuation pattern - rather than analyzing all possible parameters. By focusing on SOC change patterns during charge/discharge cycles and matching them against reference patterns, the system achieves accurate estimation with simplified processing, avoiding unnecessary complexity.
Solution Approach 2:
The patent discards detailed raw SOC data after extracting the essential fluctuation pattern information. The system recovers only the necessary estimation results from the processed data, eliminating unnecessary data retention and processing steps that would increase device complexity while maintaining estimation accuracy.
Data Source
AI summary
An estimation device 4 is provided with: an acquisition unit 41 for acquiring time-series data about an SOC of a power storage element; a specification unit 41 for specifying a fluctuation range of the SOC in the time-series data and an SOC representative value representing an SOC region in the fluctuation range; and an estimation unit 41 for estimating a shape change of the power storage element on the basis of the specified fluctuation range and the specified representative value.