Battery Module Aging Prediction for Feasible Cell Layout Design
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Solution Overview
Problem
The determination of battery aging rates in secondary batteries is time-consuming, costly, and requires significant manpower, and existing methods do not effectively predict the feasibility of battery module designs to slow down aging.
Innovation Solution
A method and system using a cell aging prediction model to correlate battery module design with battery cell aging, allowing for the estimation of negative electrode safety and feasibility of design information by predicting aging based on target design parameters, including cell and module specifications, and calculating breathing space sizes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If battery aging rates are determined through traditional testing methods, then accurate aging data can be obtained, but the process becomes time-consuming and costly
Solution Approach 1:
The patent applies preliminary action by establishing a cell aging prediction model before actual battery aging occurs. The model correlates battery module design parameters with aging outcomes, allowing designers to predict aging rates during the design phase rather than waiting for lengthy aging tests. This enables early identification of design issues and optimization opportunities.
Solution Approach 2:
The patent uses copying by creating a virtual model (cell aging prediction model) that replicates the complex aging process. Instead of physically testing batteries over extended periods, the model copies the essential aging mechanisms and relationships between design parameters and aging outcomes, providing accurate predictions without the time and resource costs of actual aging tests.
2Duration of action of stationary object
If battery module designs are optimized to slow down aging, then battery lifespan is extended, but the design process becomes more complex
Solution Approach 1:
The patent implements feedback by using the cell aging prediction model to evaluate design options and provide guidance for optimization. The model correlates design parameters with aging outcomes, giving designers feedback on how specific design choices affect battery lifespan. This enables systematic optimization of designs to extend lifespan while managing complexity through data-driven decision-making.
Solution Approach 2:
The patent applies parameter changes by identifying and optimizing key design parameters that influence aging rates. The model allows designers to adjust parameters such as cell arrangement, module structure, and thermal management configurations to slow down aging. By focusing on critical parameters rather than all possible design variables, the patent extends battery lifespan while controlling design process complexity.
3Reliability
If traditional battery aging testing is conducted, then reliable aging information is obtained, but significant manpower and resources are required
Solution Approach 1:
The patent uses copying by creating a computational model that replicates the aging process and its relationship with design parameters. This virtual copy provides reliable aging information without requiring physical batteries to undergo lengthy aging tests, dramatically reducing manpower and resource requirements while maintaining prediction accuracy.
Solution Approach 2:
The patent replaces the mechanical testing system with a computational prediction system. Instead of physically testing batteries over extended periods with significant human intervention and resource consumption, the cell aging prediction model uses computational methods to predict aging outcomes, improving resource efficiency while maintaining reliability through validated correlations between design parameters and aging behavior.
Data Source
AI summary
The present disclosure relates to a method and system for designing a battery module. The method of designing an optimal battery module may include: receiving target design information about a target battery module that includes a target battery cell; predicting aging of the target battery cell based on the target design information by using a cell aging prediction model that correlates a design of a battery module including a battery cell and aging of the battery cell; and determining whether the target design information is feasible based on the target design information and the predicted aging of the target battery cell.