Battery Testing via Pulse Voltammetry and Physics-Based Modeling
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Solution Overview
Problem
Current battery testing methods, such as electro-impedance spectroscopy, galvanostatic intermittent titration technique, and differential voltage analysis, are limited in accurately modeling internal battery parameters, leading to inaccurate state of health assessments due to rudimentary modeling and failure to effectively capture chemical and physical degradation mechanisms in Li-ion batteries.
Innovation Solution
A battery testing system and method utilizing differential pulse voltammetry (DPV) that applies a voltage pulse train and incorporates a detailed physics-based model to estimate the state of a battery by iteratively optimizing parameters like reaction rate constant, active material particle size, and Li-intercalation fraction, enabling accurate state of health evaluations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rudimentary modeling methods (electro-impedance spectroscopy, GITT, PITT) are used for battery testing, then the testing process is simple and quick, but the accuracy of state of health assessment and ability to capture internal battery parameters deteriorates
Solution Approach 1:
The patent transforms the testing approach by changing the fundamental parameters being measured and modeled. Instead of using simplified equivalent circuit models with few parameters, the invention implements a physics-based model that tracks multiple internal parameters including lithium concentration distribution, temperature fields, and mechanical stress states. This parameter transformation enables accurate state of health assessment by capturing the actual physical and chemical degradation mechanisms occurring within the battery.
Solution Approach 2:
The patent segments the battery modeling into distinct physical domains that can be independently analyzed and combined. The system divides the complex battery behavior into separate models for electrochemical reactions, heat generation, mass transport, and mechanical deformation. Each domain is modeled with appropriate physics-based equations, allowing the system to accurately capture degradation mechanisms in each domain while maintaining computational tractability.
2Measurement precision
If physics-based models with multiple parameters are used to capture degradation mechanisms, then the accuracy of degradation mode identification improves, but the difficulty of deconvoluting specific degradation modes from cycling data increases
Solution Approach 1:
The patent implements a feedback mechanism where the physics-based model continuously compares predicted battery behavior with actual measured data from cycling tests. The model parameters are adjusted iteratively to minimize the difference between predicted and observed responses. This feedback loop enables the system to deconvolute specific degradation modes by identifying which parameter changes best explain the measured data, thereby accurately identifying degradation mechanisms even in the presence of multiple simultaneous processes.
Solution Approach 2:
The patent introduces an intermediary optimization framework that acts as a mediator between the complex physics-based model and the measured cycling data. This intermediary layer uses parameter estimation techniques to bridge the gap between the detailed physical model and the simplified measurements, enabling the system to extract meaningful degradation information without directly solving the complex deconvolution problem. The intermediary optimization process transforms the difficult inverse problem into a tractable parameter estimation task.
3Ease of operation
If simple circuit-based models are used for battery testing, then the ease of operation and implementation is high, but the ability to assign physically relevant degradation modes and predict capacity fade deteriorates
Solution Approach 1:
The patent replaces the traditional electrical circuit-based modeling approach with a physics-based model grounded in fundamental electrochemical and thermodynamic principles. Instead of using equivalent circuit elements that empirically fit data, the invention uses partial differential equations describing mass transport, charge conservation, and energy balance. This substitution maintains computational efficiency while dramatically improving the ability to predict capacity fade and identify degradation modes, as the physics-based model directly represents the underlying degradation mechanisms.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides superior accuracy in estimating battery state and tracking degradation mechanisms, making it suitable for critical applications by capturing physically relevant degradation modes and providing in-situ quantification of battery health.
Implementation Method 1
A battery testing system and method utilizing differential pulse voltammetry (DPV) that applies a voltage pulse train and incorporates a detailed physics-based model to estimate the state of a battery
Data Source
AI summary
A state of battery testing system is disclosed which includes a charger, a load to be coupled across the battery's positive and negative terminals, a processer adapted to apply a predetermined voltage pulse across the battery's positive and negative terminals, apply the load to the battery, measure and log current through the load as Iexp, and establish a model based on establishing an initial estimation of state of the battery (θ0), and establishing a modeled state of battery (θi) based on a plurality of internal parameters of the battery. The model is adapted to output a model current through the load, inputting θ0 and the plurality of internal parameters of the model to thereby generate Imodel, generate an objective function (f) based on a comparison of Imodel and Iexp, and iteratively optimize θi, and output θoptimal based on the iterations.