Genetic Algorithm Cell-Fuse Placement for Battery Balance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In multi-cell batteries, small differences in cell capacity and impedance due to production tolerances or operating conditions can lead to premature failure, as weaker cells are over-stressed and stronger cells are underutilized, necessitating effective cell-fuse allocation to balance charge across cells.
Innovation Solution
A computer system employing a genetic algorithm to determine optimal cell-fuse pair placement within battery modules by generating candidate solutions based on cell and fuse data, evaluating condition values, and iteratively improving solutions through breeding operations to meet predetermined conditions for capacity and impedance balance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of stationary object
If cell balancing is implemented to equalize charge across cells, then battery life is extended, but the problem of determining optimal cell-fuse allocation becomes a complex non-linear combinatorial problem with a large number of possible combinations
Solution Approach 1:
The patent applies preliminary action by pre-sorting cells based on their capacity and impedance characteristics before assembly. This preliminary sorting organizes cells into groups that can be systematically allocated to different battery modules, reducing the combinatorial complexity of the allocation problem while ensuring that cells with similar characteristics are distributed across modules to achieve charge equalization and extend battery life
Solution Approach 2:
The patent employs parameter changes by using genetic algorithms that iteratively optimize allocation based on multiple parameters including cell capacity, impedance, module configuration, and temperature conditions. The algorithm adjusts allocation parameters across generations to find optimal solutions that balance charge distribution while managing the complexity of the allocation problem
2Manufacturing precision
If cells with small differences in capacity and impedance are used, then production tolerances are easier to meet, but these small differences are magnified with each charge or discharge cycle leading to weaker cells being over-stressed and stronger cells being under-stressed
Solution Approach 1:
The patent applies local quality by allocating cells to specific module positions based on their individual characteristics rather than treating all cells uniformly. Cells with slightly different capacities and impedances are strategically placed in different modules according to their specific properties, creating localized optimizations that prevent any single cell from being consistently over-stressed or under-stressed across charge-discharge cycles
Solution Approach 2:
The patent uses preliminary sorting and characterization of cells based on their capacity and impedance values before allocation. This preliminary assessment allows the system to identify and distribute cells with small variations in characteristics across different modules in advance, preventing the magnification of these differences during operation and maintaining more balanced stress distribution
Data Source
AI summary
A computer system that determines cell-fuse pair placement in a multi-conditional battery configuration is provided. A processor performs operations including receiving cell data, receiving fuse data, generating a population comprising a plurality of placement solutions for the battery configuration, each placement solution comprising a plurality of battery modules, each battery module comprising a plurality of virtual cells comprising a plurality of positions at which a cell-fuse pair is placed, determining, for each virtual cell of each placement solution, a condition value for each condition of the multi-conditional configuration, the condition value being based on the characteristic cell values and the characteristic fuse values, generating one or more new solutions from the population based on genetic algorithm breeding, for consideration as a survivor solution, and providing at least one of a selected survivor solution or a selected placement solution corresponding to a cell-fuse pair placement configuration satisfying a predetermined condition.