Multiple Sigmoid Model for Battery Capacity Loss Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for determining the State Of Health (SOH) of batteries, particularly lithium-ion batteries, face challenges in accurately tracking and predicting capacity loss due to factors like voltage monitoring errors caused by load-induced voltage drops and strong voltaic hysteresis in certain chemistries, which hinder precise capacity tracking and life prediction.
Innovation Solution
A multiple sigmoid model is developed by analyzing first and second degradation characteristics of electrochemical cells using sigmoid expressions, allowing for the estimation of capacity loss at desired points in time and modifying discharge or charge processes accordingly, enabling more accurate tracking and prediction of capacity fade.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If voltage monitoring is used to estimate battery capacity, then the method is simple to implement, but measurement precision deteriorates due to load-induced voltage drops and internal impedance
Solution Approach 1:
The patent segments the capacity estimation process into multiple components: open-circuit voltage estimation (separate from load voltage), correction terms for voltage drops, and integration with current measurements. This segmentation allows each component to be optimized independently, resolving the contradiction between simplicity and precision.
Solution Approach 2:
The patent introduces an intermediary correction mechanism that mediates between the simple voltage measurement and the accurate capacity estimation. The correction terms act as intermediaries that compensate for the harmful effects of internal impedance and voltage drops, enabling both simplicity and precision.
2Duration of action of moving object
If current integration (coulomb counting) is used to track battery capacity, then capacity tracking is continuous, but accuracy deteriorates due to accumulation of measurement errors over time
Solution Approach 1:
The patent implements feedback by continuously comparing the integrated capacity with voltage-based capacity estimates and using the difference to correct the integration. This feedback mechanism prevents error accumulation while maintaining continuous tracking, resolving the contradiction between duration and precision.
Solution Approach 2:
The patent performs preliminary calibration of the current integration using voltage-based methods before significant error accumulation occurs. This preliminary action establishes an accurate baseline that prevents subsequent error propagation, enabling both continuous tracking and high accuracy.
3Device complexity
If single sigmoid model is used for capacity loss prediction, then the model is simple, but prediction accuracy deteriorates for complex degradation patterns
Solution Approach 1:
The patent segments the capacity loss into multiple degradation components, each modeled by a separate sigmoid function with different time constants and asymptotes. This segmentation captures complex degradation patterns while keeping individual model components simple, resolving the contradiction between model complexity and prediction accuracy.
Solution Approach 2:
The patent creates a composite prediction model by combining multiple sigmoid functions, each representing different degradation mechanisms. This composite approach captures the complexity of real battery degradation while maintaining the mathematical simplicity of individual sigmoid components.
Data Source
AI summary
A system includes an electrochemical cell, monitoring hardware, and a computing system. The monitoring hardware periodically samples charge characteristics of the electrochemical cell. The computing system periodically determines cell information from the charge characteristics of the electrochemical cell. The computing system also periodically adds a first degradation characteristic from the cell information to a first sigmoid expression, periodically adds a second degradation characteristic from the cell information to a second sigmoid expression and combines the first sigmoid expression and the second sigmoid expression to develop or augment a multiple sigmoid model (MSM) of the electrochemical cell. The MSM may be used to estimate a capacity loss of the electrochemical cell at a desired point in time and analyze other characteristics of the electrochemical cell. The first and second degradation characteristics may be loss of active host sites and loss of free lithium for Li-ion cells.