Battery State Estimation Using Segmented Electrochemical Modeling
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Solution Overview
Problem
Current battery state estimation methods, such as coulomb counting and battery models, face challenges in accurately determining the state of charge (SOC) and state of health (SOH) of batteries, especially in complex battery packs with multiple cells, leading to inaccuracies and increased calculation times.
Innovation Solution
A method and apparatus that classify batteries into target and non-target batteries using electrochemical models, determine state information through sensing data, and apply state change amounts to correct SOC estimates across switching periods, utilizing a controller and estimators to selectively apply different battery models for accurate SOC estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If voltage-based methods are used to estimate SOC, then calculation time is reduced, but measurement precision deteriorates leading to inaccuracies
Solution Approach 1:
The patent segments the battery pack into multiple battery cells, with at least one target battery cell estimated using a detailed electrochemical model and other battery cells estimated using simplified methods. This segmentation allows the system to achieve high accuracy for critical cells while maintaining fast calculation speeds for the overall battery pack SOC estimation.
Solution Approach 2:
The patent applies different estimation qualities to different battery cells based on their roles. Target battery cells that significantly affect overall SOC are estimated with high precision using electrochemical models, while non-target cells use faster voltage-based methods. This local quality differentiation resolves the contradiction between accuracy and speed.
2Measurement precision
If electrochemical models are applied to all batteries, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent divides the battery pack into target and non-target battery cells, applying electrochemical models only to target cells. This segmentation reduces computational complexity while maintaining accuracy for cells that most influence overall SOC, resolving the contradiction between precision and complexity.
Solution Approach 2:
The patent dynamically changes parameters by identifying which battery cells are target cells based on their impact on overall SOC. This parameter change approach allows the system to adapt the level of modeling complexity to the actual needs of each operating condition, reducing overall device complexity while maintaining necessary precision.
3Measurement precision
If multiple battery models are used for different batteries, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent segments battery cells into target and non-target groups, applying different models appropriately. This segmentation simplifies model management by clearly defining which cells require complex electrochemical models versus simpler voltage-based methods, reducing model management complexity while maintaining accuracy.
Solution Approach 2:
The patent applies different model qualities locally to different battery cells based on their specific roles and impact on overall SOC. This local quality approach ensures that complex models are only applied where necessary, improving precision for critical cells while keeping the overall system manageable.
Data Source
AI summary
Disclosed is a battery state estimation method and apparatus, the method including selecting sensing data of a portion of batteries, transmitting the selected sensing data to at least one estimator among estimators, transmitting sensing data of a remaining portion of the batteries to a remaining estimator among the estimators, and determining state information of the batteries using the estimators.