Battery Capacity Estimation Using Charging Interval Features

Solution Overview

Problem

Existing battery capacity estimation methods for lithium-ion batteries, particularly as they age, require large amounts of data and time, leading to inaccuracies and potential shutdowns due to capacity jumps.

Innovation Solution

A method utilizing a Gaussian process regression model for battery capacity estimation based on charging current, voltage, and time intervals, combined with electrochemical and time domain features, reduces data requirements and estimation time by using training features derived from standard battery cycles and actual battery data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If complete experimental cycling data is used for battery capacity estimation, then estimation accuracy is improved, but data amount and estimation time increase significantly

Engineering Contradiction:

Improvebattery capacity estimation accuracyVSAvoidestimation time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent extracts only the most relevant features from complete experimental cycling data, specifically selecting electrochemical features (dQ/dV peak values, positions, areas) and time domain features (charging time, voltage intervals) that have the strongest correlation with battery capacity. This extraction approach maintains estimation accuracy while dramatically reducing the data processing burden and estimation time.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent creates a simplified copy of the complete experimental cycling data by using only the essential features needed for capacity estimation. Instead of processing all raw cycling data, the method uses a condensed representation containing key electrochemical and time domain characteristics, achieving the same estimation goal with minimal data.

Inventive Principle:

Principle #26Copying

2Measurement precision

If complete experimental cycling data is used for battery capacity estimation, then estimation accuracy is improved, but the amount of data required increases

Engineering Contradiction:

Improvebattery capacity estimation accuracyVSAvoiddata amount

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The patent extracts only the most relevant features from complete experimental cycling data, specifically selecting electrochemical features (dQ/dV peak values, positions, areas) and time domain features (charging time, voltage intervals) that have the strongest correlation with battery capacity. This extraction approach maintains estimation accuracy while dramatically reducing the data processing burden and estimation time.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent creates a simplified copy of the complete experimental cycling data by using only the essential features needed for capacity estimation. Instead of processing all raw cycling data, the method uses a condensed representation containing key electrochemical and time domain characteristics, achieving the same estimation goal with minimal data.

Inventive Principle:

Principle #26Copying

3Device complexity

If traditional capacity estimation methods are used for aged batteries, then computational simplicity is maintained, but capacity jump and shutdown occur due to accuracy degradation

Engineering Contradiction:

Improveestimation method complexityVSAvoidbattery operation stability

Core Design Contradiction:

Device complexityVSReliability

Solution Approach 1:

The patent performs preliminary analysis of the charging curve to identify key electrochemical features (dQ/dV peaks) and time domain characteristics before capacity estimation. By pre-processing the data to extract these critical features and establishing their relationship with capacity in advance, the method achieves high accuracy for aged batteries while maintaining computational efficiency during actual operation.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent changes the estimation parameters from simple capacity calculations to multi-feature analysis including electrochemical features (dQ/dV characteristics) and time domain features (charging time, voltage intervals). This parameter transformation enables the system to adapt to battery aging effects and prevent capacity jumps, improving reliability without excessive complexity.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12411177B2Method for battery capacity estimation, electronic apparatus, and storage medium

Publication Date: 2025.09.09 DONGGUAN NVT TECH

AI summary

A method of battery capacity estimation including: obtaining a charging current, charging voltage, charging time, and charging voltage interval of a battery being estimated; determining a charging current interval and a charging time interval based on the charging current, the charging voltage, and the charging time, where the charging current interval and the charging time interval correspond to the charging voltage interval; obtaining training features for model training, where the training features include training electrochemical features and training time domain features; determining actual features of the battery being estimated based on the charging voltage interval, the charging current interval, the charging time interval, and the training features, where the actual features include actual electrochemical features and actual time domain features; and estimating, based on the model and the actual features, an actual capacity of the battery being estimated.