Battery Degradation Prediction Using Digital Operation Models
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Solution Overview
Problem
Conventional storage battery systems face challenges in accurately predicting and managing battery degradation due to varying temperature and operation conditions, which affects battery capacity and overall system performance.
Innovation Solution
A storage battery management device with a hardware processor that acquires and estimates battery characteristics using a digital model to simulate operations and deterioration, allowing for predictive maintenance and efficient management of battery units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional prediction techniques based on temperature and SOC are used, then battery deterioration can be predicted, but the prediction accuracy is insufficient due to varying operation conditions
Solution Approach 1:
The patent transforms raw operation data into standardized operation condition data by changing parameters (normalizing temperature, SOC, current, and time data). This parameter transformation enables accurate comparison across different operation conditions while maintaining manageable data complexity through systematic normalization processes.
Solution Approach 2:
The patent creates a virtual copy of the battery's operation history by generating synthetic operation data that replicates real usage patterns. This copying approach allows the system to analyze multiple hypothetical scenarios without requiring additional physical testing, improving prediction accuracy while avoiding the complexity of extensive physical experimentation.
2Loss of information
If detailed battery monitoring is implemented to improve degradation prediction, then battery health insights are enhanced, but data processing complexity increases
Solution Approach 1:
The patent extracts only the essential operation condition parameters (temperature, SOC, current, time) from the comprehensive battery data set. By selectively extracting these key parameters and excluding redundant information, the system maintains complete and accurate degradation prediction capabilities while significantly reducing data processing complexity.
Solution Approach 2:
The patent segments the battery monitoring system into distinct functional modules: data acquisition, parameter transformation, operation condition data generation, and degradation prediction. This segmentation allows each module to process specific tasks independently, reducing overall system complexity while maintaining comprehensive monitoring capabilities.
Data Source
AI summary
A storage battery management device includes an acquisition unit, a selection unit, an estimation unit, and a display control unit. The acquisition unit acquires a battery characteristic and an operation condition of a storage battery device. The selection unit selects data values from a data value group of a data item in which variation in data value is caused out of data items included in the battery characteristic and the acquired operation condition. The estimation unit estimates, for each of the selected data values, a battery characteristic after operation corresponding to a case where the storage battery device is operated under the operation condition. The battery characteristic is estimated on the basis of the battery characteristic and the operation condition acquired by the acquisition unit. The display control unit displays the battery characteristic estimated by the estimation unit in a comparable state.