Battery Energy Storage Container Modeling for Multi-State Mixing Precision
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Solution Overview
Problem
Existing battery energy storage systems lack accurate methods to model the multi-state health conditions of battery cells, leading to uncertainties in reliability evaluation, which affects the safety and performance of the system.
Innovation Solution
A method and system for modeling multi-state mixing precision of battery energy storage containers, dividing the system into four levels (battery cell, pack, cluster, and compartment) with five health states (excellence, attenuation, risk, defect, fault), constructing normal distribution models, and using universal generating functions to model the system's reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a multi-state model is constructed to accurately describe battery health states, then reliability evaluation precision is improved, but model complexity increases
Solution Approach 1:
The battery system is segmented into five distinct health states (excellent, attenuation, risk, defect, fault) based on SOH ranges, allowing complex battery behavior to be modeled through discrete state transitions rather than continuous complex functions. This segmentation simplifies the modeling approach while maintaining evaluation precision.
Solution Approach 2:
The patent introduces a hierarchical four-level structure (battery cell, battery pack, battery cluster, battery compartment) that adds a spatial dimension to the modeling. This dimensional approach allows the complex multi-state problem to be decomposed into manageable hierarchical levels, reducing overall model complexity while preserving reliability evaluation accuracy.
2Measurement precision
If a four-level hierarchical model is constructed to represent battery structure, then modeling precision is improved, but calculation complexity increases
Solution Approach 1:
The battery system is divided into four hierarchical levels (battery cell, battery pack, battery cluster, battery compartment), each with its own state probability distributions. This segmentation allows complex system-level calculations to be broken down into independent lower-level calculations, improving modeling precision while managing calculation complexity through modular computation.
Solution Approach 2:
The patent merges the state probabilities from individual battery cells through the hierarchical structure to obtain system-level reliability metrics. By combining results from lower levels (cell → pack → cluster, compartment, system), the model achieves high precision without requiring exponentially complex calculations at each level.
3Reliability
If normal distribution models are constructed for SOH probabilities, then reliability evaluation accuracy is improved, but computational time increases
Solution Approach 1:
The patent transforms the continuous SOH parameter into discrete state probabilities using normal distribution functions. By changing from continuous parameter analysis to discrete state probability calculations, the model achieves improved reliability evaluation accuracy while reducing computational complexity compared to continuous simulation methods.
Data Source
AI summary
A method, a system and a device for modeling multi-state mixing precision of a battery energy storage container are provided. The method includes: dividing a battery energy storage container into a four-level model; dividing states of the battery cells into five states according to SOHs of the battery cells; constructing a normal distribution model of the SOHs and determining the probability of each battery cell in each state; constructing a universal generating function of the battery cells; constructing the universal generating function of a battery pack, the universal generating function of a battery cluster, and the universal generating function of a battery compartment; and constructing an overall model of a five-state and four-level mixing precision energy storage battery compartment based on each universal generating function and an internal topological structure of the battery energy storage container.