Step Parameter Identification for Lithium-Ion Battery Electrochemical Models
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Solution Overview
Problem
Current electrochemical models for lithium-ion batteries, such as Lumped Particle Model (LPM) and Single Particle Model (SPM), are inefficient for high-dimensional parameter identification due to simplification, leading to reduced accuracy and increased computation time, especially at higher discharge rates.
Innovation Solution
A method for step parameter identification using a reduced-order electrochemical model to extrapolate target parameters, which are then applied to a full-order model, improving efficiency by simplifying the parameter identification process and enhancing accuracy across various charge-discharge rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a full-order electrochemical model is used for parameter identification, then measurement precision is improved, but loss of time increases due to high-dimensional parameter space and computational complexity
Solution Approach 1:
The parameter identification process is divided into two stages: first identifying a subset of parameters (first type) using a reduced-order model, then using those results as initial values for identifying remaining parameters (second type) in the full-order model. This segmentation reduces the overall computational burden while maintaining accuracy.
Solution Approach 2:
The reduced-order model is used to perform preliminary parameter identification before applying results to the full-order model. This preliminary action provides good initial estimates that accelerate convergence and reduce computation time for the subsequent full-order model identification.
2Loss of time
If reduced-order electrochemical models (LPM or SPM) are used for parameter identification, then loss of time is reduced, but manufacturing precision deteriorates due to model simplification
Solution Approach 1:
The model is used in two sequential stages: the reduced-order model handles initial parameter identification where computation speed is critical, then the full-order model completes the identification where accuracy is critical. This segmentation allows each model to be used in its optimal performance regime.
Solution Approach 2:
The reduced-order model acts as an intermediary that prepares initial parameter estimates, which then serve as input for the full-order model. This intermediary step bridges the gap between computational efficiency and identification accuracy.
Data Source
AI summary
The invention discloses a method for step parameter identification based on an electrochemical model, including: identifying a first type of electrochemical parameters through a reduced-order electrochemical model by utilizing different preset charge-discharge rate data; determining a first type of target electrochemical parameters identified by the reduced-order electrochemical model through extrapolation; the first type of target electrochemical parameters including an approximate value of the first type of electrochemical parameters or the first type of electrochemical parameters at a specific rate; identifying the first type of electrochemical parameters, which includes solid-phase lithium concentration and exchange current, through a full-order electrochemical model by taking the first type of target electrochemical parameters as actual values of the first type of electrochemical parameters of the full-order electrochemical model; and identifying a second type of electrochemical parameters, which includes liquid-phase potential and liquid-phase lithium concentration, through the full-order electrochemical model.