Energy Storage Power Load Modeling for Capacity Transition Prediction
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Solution Overview
Problem
Existing energy storage devices in industrial applications lack accurate methods to measure and analyze power loads over extended periods, leading to inaccurate capacity transition predictions due to fluctuating usage patterns.
Innovation Solution
An information processing device that acquires time-series power data, classifies it into shorter periods, extracts representative power loads, and generates virtual power loads by combining these representative loads, reflecting potential usage changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If power load measurement is performed over a short period (charge-discharge cycle), then measurement simplicity is improved, but prediction accuracy of energy storage device life deteriorates
Solution Approach 1:
The patent segments the power load measurement period into multiple shorter intervals (e.g., daily, weekly, monthly periods) within a longer overall measurement period. This allows the system to maintain measurement simplicity by using short-term data collection while improving prediction accuracy by analyzing trends across extended timeframes through classification of power load patterns into different usage periods.
2Device complexity
If direct use of measurement result from certain period is applied, then processing simplicity is improved, but adaptability to usage changes deteriorates
Solution Approach 1:
The patent implements dynamic adaptability by classifying power load patterns into different usage periods (e.g., initial period, steady-state period, changing period) and selectively applying different processing methods for each classification. This allows the system to adapt to usage changes automatically while maintaining relatively simple processing through rule-based classification rather than complex real-time adjustments.
3Measurement precision
If power load is classified into multiple groups, then analysis accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent segments power load data into multiple classification groups based on usage patterns (e.g., charge-discharge cycles, idle periods, high-load periods). This segmentation improves analysis accuracy by enabling separate processing of different operational modes while controlling processing complexity through systematic classification rules and automated grouping algorithms that reduce manual intervention requirements.
Data Source
AI summary
An information processing device includes an acquisition unit that acquires a power load indicating time-series data of power of an energy storage device in a first period, a classification unit that classifies each power load obtained by dividing a power load acquired by the acquisition unit for each second period shorter than the first period into a plurality of groups, and an extraction unit that extracts, for each group classified by the classification unit, a representative power load from among power loads for each second period belonging to each group. An information processing device includes an acquisition unit that acquires a plurality of representative power loads extracted from among power loads obtained by dividing a power load indicating time-series data of power of an energy storage device in a first period for each second period shorter than the first period, and a generation unit that generates a virtual power load in the first period by combining a plurality of representative power loads acquired by the acquisition unit.