Battery Cell State Estimation Using Thermal Perturbation Spectra
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Solution Overview
Problem
Traditional methods of measuring the entropy coefficient and state of charge (SOC) of lithium-ion batteries are time-consuming and prone to drift, necessitating a faster and more accurate method to understand heat generation and optimize battery performance.
Innovation Solution
A computer-implemented method using Peltier elements to apply temperature perturbations to battery cell groupings, measuring voltage and temperature responses, and transforming these signals into the frequency domain to calculate entropy coefficients, allowing for precise state of charge estimation and heat prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to measure entropy coefficient and state of charge, then measurement accuracy can be maintained, but measurement time becomes prohibitively long
Solution Approach 1:
The patent applies periodic temperature perturbations (heating and cooling cycles) to the battery cell using Peltier elements. By subjecting the cell to repeated thermal cycles and measuring the voltage response during these periodic actions, the system extracts entropy coefficient information through frequency domain analysis, achieving accurate measurements without prohibitively long measurement times
Solution Approach 2:
The patent applies thermal vibration by subjecting the battery cell to oscillating temperature perturbations. Through frequency domain analysis of the voltage response to these thermal vibrations, the system determines the entropy coefficient rapidly, transforming a static measurement problem into a dynamic vibration-based measurement approach
2Loss of information
If traditional measurement methods are used, then comprehensive battery state information can be obtained, but voltage drift occurs during measurement
Solution Approach 1:
The patent introduces temperature perturbation as an intermediary stimulus and uses frequency domain analysis as an intermediary processing method. By measuring the voltage response to controlled temperature variations and analyzing it through frequency domain techniques, the system extracts entropy coefficient information while avoiding the voltage drift problems of traditional direct measurement methods
Solution Approach 2:
The patent replaces traditional time-domain voltage measurement methods with frequency-domain analysis of temperature-voltage coupling responses. This substitution transforms the measurement approach from direct voltage monitoring (prone to drift) to spectral analysis of perturbed responses, eliminating voltage drift issues while maintaining information completeness
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
Enables rapid and accurate determination of battery state and heat generation, enhancing performance optimization and preventing thermal events by accounting for reversible heat sources.
Implementation Method 1
applying the perturbation to the cell grouping using Peltier elements
Implementation Method 2
The entropy coefficient of the cell grouping is calculated using a Fourier transform of the measured perturbation temperature of the cell grouping and the measured voltage of the cell grouping
Data Source
AI summary
A method of battery cell state estimation includes measuring an initial temperature of a cell grouping in a vehicle, applying a perturbation to the cell grouping in the vehicle for a threshold period of time, and measuring a perturbation temperature of the cell grouping and a voltage of the cell grouping. The method also includes calculating, based on the measured perturbation temperature of the cell grouping and the measured voltage of the cell grouping, an entropy coefficient of the cell grouping, and determining a plateau location based on the measured voltage of the cell grouping. The method further includes generating a state of charge estimate based on the entropy coefficient and the plateau location, and splitting the state of charge estimate and the entropy coefficient into a material level state of lithiation and a material level entropy coefficient.