Battery Component ID Models for Accurate State of Charge Estimation
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Solution Overview
Problem
Existing battery state of charge estimation models do not accurately account for differences in the type and number of components, leading to suboptimal estimation accuracy.
Innovation Solution
A method for generating a trained model that considers the identification information of each component hierarchy in a battery, allowing for the acquisition and processing of specific operating data to create models tailored to each component configuration, thereby improving estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single nonlinear regression model is used for all battery types, then the model structure is simple and easy to implement, but the estimation accuracy of state of charge deteriorates due to not accounting for differences in component type and number
Solution Approach 1:
The patent segments the battery system into hierarchical components (battery pack, module, block, cell) and creates separate trained models for each component type. This segmentation allows each model to be specialized for specific component configurations, improving estimation accuracy while managing complexity through modular model structure.
Solution Approach 2:
The patent applies local quality by creating customized trained models for each specific component type and configuration rather than using a uniform model for all batteries. Each model is locally optimized for its specific component characteristics, enabling high-accuracy estimation tailored to particular battery configurations.
2Measurement precision
If separate trained models are generated for each component configuration, then the estimation accuracy improves, but the number of models and data processing requirements increase
Solution Approach 1:
The patent creates a universal framework where trained models can be applied across different battery configurations. The hierarchical component-based approach allows models to be reused and combined in various configurations, reducing the total number of models needed while maintaining high accuracy for diverse battery types.
Solution Approach 2:
The patent uses parameter changes by varying the identification information (component type, number of cells, connection configuration) to generate appropriate trained models. This allows the system to adapt to different battery configurations by changing model parameters rather than creating entirely separate models for each scenario.
Data Source
AI summary
A generation device includes: an acquisition unit that acquires one or more pieces of identification information imparted to a component in a certain hierarchy among a plurality of components, the one or more pieces of identification information being identification information identifiably imparted with the type and the number of components in a lower hierarchy; a generation unit that generates a trained model corresponding to each piece of identification information for estimating a state of a battery by learning operating data for each of the one or more pieces of identification information; and an output unit that outputs the generated trained model.