Lithium Battery State Prediction via Fourier Laplace Transforms
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Solution Overview
Problem
Current methods for predicting the working conditions of lithium batteries are inaccurate and computationally intensive, leading to potential thermal safety issues due to the use of approximate and simplified solutions in electrochemical model simulations.
Innovation Solution
The method involves performing a Fourier transform on the physicochemical state quantity distribution function of a solid-phase lithium battery to calculate a series function in the frequency domain, followed by a Laplace transform to obtain an analytical solution in the time domain, allowing for precise prediction of working conditions at any location and time, and generating early warnings when thresholds are exceeded.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If finite difference methods, finite element methods, or finite volume methods are used to simulate electrochemical models, then the simulation can capture detailed physical and chemical processes, but the computational power requirement increases and calculation speed decreases
Solution Approach 1:
The patent replaces traditional mechanical numerical simulation methods (finite difference, finite element, finite volume) with an analytical solution approach based on mathematical transforms. This substitution eliminates the need for iterative numerical calculations while maintaining simulation accuracy, thereby dramatically improving calculation speed and reducing computational power requirements.
Solution Approach 2:
The patent transforms the governing partial differential equations into the frequency domain using Fourier transform and solves them analytically. This parameter transformation approach changes the mathematical representation from spatial-temporal domain to frequency domain, enabling closed-form solutions that are computationally efficient and avoid the pitfalls of numerical iteration.
2Productivity
If fitting function methods or simplified physical and chemical control conditions are used, then the calculation is faster and computational power requirement is reduced, but the accuracy of the solution decreases and cumulative errors occur
Solution Approach 1:
The patent replaces approximate fitting function methods with an exact analytical solution method. By using Fourier transform to convert the governing equations into the frequency domain and solving them analytically, the patent achieves both high calculation speed and high solution accuracy without the cumulative errors inherent in iterative approximation methods.
3Device complexity
If threshold judgment or black box machine learning methods are used for battery early warning, then the implementation is simpler, but the prediction accuracy of internal physical quantity changes decreases
Solution Approach 1:
The patent introduces an analytical electrochemical model as an intermediary between simple threshold judgment and complex black box machine learning. This model-based approach uses Fourier transform to accurately predict internal physical quantity changes while maintaining reasonable algorithmic complexity, providing a middle ground that combines interpretability with prediction accuracy.
Data Source
AI summary
The invention discloses method and system for predicting working conditions of lithium batteries. The method includes performing a Fourier transform on a physicochemical state quantity distribution function of a solid-phase lithium battery to calculate a physicochemical state quantity distribution series function in a frequency domain and obtain a solid-phase physicochemical state quantity in the frequency domain according to the physicochemical state quantity distribution series function; performing a Laplace transform on a partial differential governing equation set of the solid-phase physicochemical state quantity to obtain a solid-phase ordinary differential equation set in a complex frequency domain and obtain an analytical solution in the time domain through an inverse Laplace transform; and calculating, according to the analytical solution, a predicted value of the working conditions of the solid-phase lithium battery at any location and at any time in the future.