Battery State Estimation via Cell Partitioning
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Solution Overview
Problem
Existing battery management systems face inaccuracies and reliability issues in estimating battery state due to manufacturing inconsistencies and varying aging degrees among cells, leading to potential safety problems.
Innovation Solution
A battery state estimation method that partitions cells based on their state parameters, using target parameters for each partition to calculate and adjust cell states, eliminating the need for a unified standard and improving estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a unified standard is used to estimate battery state for all cells, then the management process is simplified, but the estimation accuracy deteriorates due to manufacturing inconsistencies and varying aging degrees among cells
Solution Approach 1:
The patent segments the battery management approach by dividing cells into different groups based on their state parameters (such as capacity, internal resistance, and aging degree). Each group is managed with tailored estimation parameters rather than a unified standard, thereby resolving the contradiction between management simplicity and estimation accuracy.
Solution Approach 2:
The patent applies local quality by assigning different estimation parameters and methods to different cell groups according to their specific characteristics. Cells with similar states share common parameters, while cells with divergent states receive customized parameters, ensuring high estimation accuracy for each local group without requiring complete individual customization for every cell.
2Measurement precision
If individual cell variations are accounted for in battery state estimation, then estimation accuracy is improved, but the complexity of the management system increases
Solution Approach 1:
The patent reduces system complexity by segmenting cells into groups with similar characteristics, so that individual cell variations are accounted for without requiring completely independent management of each cell. This grouping approach captures the essential variations while maintaining manageable system complexity.
Solution Approach 2:
The patent manages complexity by dynamically adjusting estimation parameters based on cell group characteristics rather than using fixed parameters for all cells. This allows the system to adapt to individual variations through parameter changes within a structured framework, balancing accuracy requirements with system complexity constraints.
Data Source
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AI summary
The present application provides a battery state estimation method, which relates to the field of battery technology. The battery state estimation method includes: obtaining a first cell state parameter of each of a plurality of cells in a battery; under a condition that there are multiple cell partitions, determining whether the first cell state parameter of each cell belonging to a cell partition is within a first cell state parameter range corresponding to the cell partition; when it is within the first cell state parameter range corresponding to the cell partition, estimating a cell state of each of the cell partitions based on a target cell state parameter corresponding to the cell partition; and estimating a battery state of the battery based on the cell state; wherein a deviation of the first cell state parameter among individual cells belonging to a same cell partition is within a first deviation range. The battery state estimation method provided by the embodiments of the present application can reduce the error of the battery state estimation and improve the accuracy of the battery state estimation.