Likelihood Function for Lithium Battery Parameter Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current lithium-ion battery models, particularly the equivalent circuit model, face challenges in accurately identifying third-order model parameters due to data saturation issues with increasing data, which affects the precision of battery performance estimation.
Innovation Solution
A method and system that utilize a likelihood function to identify third-order model parameters of lithium batteries by collecting data on output voltage and total battery current under various temperatures and charge-discharge conditions, applying maximum likelihood estimation to calculate relevant parameters, including ohmic resistors, charge transfer components, and diffused capacitors and resistors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If least squares estimation is used to identify battery parameters, then the estimation is based on fixed sample data, but data saturation occurs when data increases which reduces estimation accuracy
Solution Approach 1:
The patent changes the estimation methodology from least squares to maximum likelihood estimation, fundamentally altering the parameter estimation approach. This allows the system to handle increasing data volumes without saturation by using probabilistic modeling that naturally accommodates larger datasets through the likelihood function formulation.
Solution Approach 2:
The patent replaces the deterministic least squares mechanical approach with a probabilistic maximum likelihood framework. This substitution introduces statistical theory to handle data saturation issues, where the likelihood function naturally manages large sample sizes without the saturation problems that plague traditional least squares methods.
2Measurement precision
If higher-order battery models are used to improve accuracy, then the model closer to actual battery behavior, but the model structure becomes more complex
Solution Approach 1:
The patent changes the third-order model parameters from fixed or简单地 fitted values to those identified through maximum likelihood estimation. This parameter transformation enables the third-order model to achieve accuracy comparable to higher-order models by optimizing parameters using statistical theory, thereby maintaining model simplicity while improving precision.
Solution Approach 2:
The patent creates an equivalent representation of complex battery behavior using a simplified third-order model structure. By using maximum likelihood estimation to identify parameters, the simpler model copies the essential dynamic characteristics of more complex models without requiring their structural complexity, achieving the same predictive accuracy with fewer elements.
3Reliability
If more data is collected for parameter identification, then the sample size increases improving statistical reliability, but least squares estimation suffers from data saturation reducing precision
Solution Approach 1:
The patent substitutes the least squares estimation mechanism with maximum likelihood estimation, which is inherently designed to handle large sample sizes. The likelihood function formulation naturally incorporates all available data points without saturation, allowing the system to fully utilize large datasets to improve both reliability and precision simultaneously.
Solution Approach 2:
The patent introduces dynamic adaptability to the estimation process through maximum likelihood estimation. Unlike fixed least squares approaches, the likelihood function dynamically adjusts parameter estimates based on the entire dataset distribution, allowing optimal use of increasing data volumes without the static saturation limitations of traditional methods.
Data Source
AI summary
A method and a system for identifying third-order model parameters of a lithium battery based on a likelihood function are provided, which relates to a method for estimating battery model parameters of a lithium battery under different temperatures, different system-on-chips (SOCs), and charge-discharge currents. The method includes the following steps. A third-order battery model of the lithium battery is established. A battery model output voltage Ud and a total battery current I under different temperatures, different SOCs, and charge-discharge currents are collected. The likelihood function is adopted to construct an identification model, and the collected data is substituted into the identification model to calculate the battery model parameters. Identified parameters are substituted into the third-order battery model to obtain a battery terminal voltage to be compared with a measured terminal voltage. The operation method of the disclosure is simple and effective, and can accurately estimate internal resistance parameters of the lithium battery.