Energy Storage Battery Cell Optimization via Hierarchical Segmentation
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Solution Overview
Problem
Existing energy storage systems face challenges in accurately determining the actual state of batteries, leading to ineffective problem identification and poor optimization, which hinders efficient system performance.
Innovation Solution
A method and apparatus that acquire configuration information of battery cells, compute actual state information, and determine a to-be-optimized state at a granular level, allowing for accurate judgment and timely identification of issues through optimization groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual monitoring method is used to track battery parameters, then operation simplicity is maintained, but measurement precision of actual battery state deteriorates
Solution Approach 1:
The patent segments the energy storage system into hierarchical levels (battery cells → battery clusters → sub-systems → containers), enabling precise monitoring at each level. By dividing the system into manageable segments with dedicated monitoring units, the patent achieves accurate battery state judgment without requiring a monolithic complex monitoring system.
Solution Approach 2:
The patent introduces optimization groups as intermediary entities that bridge the gap between raw battery parameter monitoring and actual battery state determination. These optimization groups process and analyze data from multiple battery cells, providing a mediator layer that enhances measurement precision while managing system complexity through structured data organization.
2Measurement precision
If detailed configuration information is collected from each battery cell, then measurement precision improves, but loss of time in data processing increases
Solution Approach 1:
The patent performs preliminary actions by pre-establishing optimization groups and pre-processing configuration information during system setup and idle periods. Battery cell data is organized into optimization groups in advance, and baseline performance metrics are calculated beforehand, enabling rapid response when optimization is actually needed without time-consuming processing during critical moments.
Solution Approach 2:
The patent applies partial action by focusing monitoring and optimization efforts on specific optimization groups rather than processing all battery cell data uniformly. By identifying and concentrating resources on particular groups that require optimization, the system achieves accurate battery state judgment for critical areas without the time penalty of comprehensive system-wide analysis.
3Manufacturing precision
If optimization is performed at system level only, then device complexity is reduced, but manufacturing precision of optimization strategy deteriorates
Solution Approach 1:
The patent segments the optimization process into hierarchical levels matching the physical system structure. Each level (battery cells, clusters, sub-systems, containers) has its own optimization strategies tailored to its specific characteristics. This segmentation enables precise optimization strategies for each segment while maintaining overall system coherence, avoiding the need for a single complex system-level optimization approach.
Solution Approach 2:
The patent implements local quality by allowing different optimization strategies to be applied to different optimization groups based on their specific conditions and requirements. Each group can have customized optimization parameters and thresholds, enabling precise, localized optimization decisions rather than applying uniform system-level strategies that cannot account for local variations in battery performance and state.
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AI summary
The present application provides a method and apparatus for optimizing an energy storage system, a device, a storage medium and a program product. The method for optimizing the energy storage system includes: acquiring configuration information of each of battery cells in a target station corresponding to a detection instruction in response to the detection instruction; computing actual state information of each of the battery cells according to the configuration information; determining a to-be-optimized state of a group to which each of the battery cells belongs according to the actual state information of each of the battery cells; in response to an optimization instruction, determining a target optimization group from groups and optimizing the battery cell in the target optimization group. According to the present application, a granularity of a judgment basis for a final determination of the to-be-optimized state becomes relatively smaller, a judgment result becomes more accurate, so problems of the energy storage system can be found timely and effectively, and a better optimization effect can be achieved.