Battery Internal State Estimation Using Layered SPM Diffusion Modeling
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Solution Overview
Problem
Current methods for estimating the internal state of batteries, such as state of charge (SOC) and state of health, lack accuracy and efficiency in real-time monitoring due to limitations in numerical modeling and understanding of lithium-ion secondary battery phenomena.
Innovation Solution
A method and apparatus using an electrochemical model based on the single particle model (SPM) to estimate the internal state of batteries by calculating inner lithium-ion concentrations in cathodes and anodes, considering diffusion differences and temperature, and estimating voltage based on overpotential values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If numerical modeling methods are used to estimate battery internal state, then the estimation can be performed, but the accuracy and efficiency in real-time monitoring are insufficient
Solution Approach 1:
The battery electrode particles are segmented into multiple concentric layers (e.g., 3-10 layers) radiating from the center to the surface. This segmentation allows the model to calculate lithium-ion concentration at each layer independently, capturing the spatial distribution dynamics while maintaining computational efficiency for real-time monitoring.
Solution Approach 2:
The model transitions from traditional one-dimensional average concentration modeling to a multi-dimensional approach by considering radial distance from particle center to surface. This adds the spatial dimension of concentration distribution, enabling more accurate estimation of internal battery states while maintaining real-time computational capability through the layered structure.
2Measurement precision
If detailed electrochemical modeling is implemented to improve accuracy, then estimation precision improves, but computational complexity increases
Solution Approach 1:
By dividing particles into discrete layers, the complex partial differential equations of lithium-ion diffusion are transformed into a series of simpler algebraic equations that can be solved sequentially from the center layer outward, reducing computational complexity while maintaining accuracy.
Solution Approach 2:
The model pre-establishes the layered structure and diffusion pathways before real-time operation. During monitoring, it only needs to update concentration values at each layer based on current conditions, avoiding the need to solve complex differential equations from scratch and thus reducing real-time computational burden.
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 accurate and efficient real-time estimation of battery internal states, improving monitoring and management by simulating lithium-ion concentration and voltage dynamics within the battery.
Implementation Method 1
calculate inner lithium-ion concentrations in the cathode and the anode based on a difference between diffusion of lithium ions in the cathode and the anode of the battery
Implementation Method 2
estimates a voltage of the battery based on an overpotential value defined as a difference between a measured voltage of the battery and an open-circuit voltage (OCV) of the battery
Data Source
AI summary
A method of estimating an internal state of a battery includes acquiring at least one parameter of the battery, and estimating the internal state of the battery by using an electrochemical model based on the at least one parameter, the electrochemical model being calculated based on a single particle model (SPM) with respect to a cathode and an anode of the battery, wherein the electrochemical model includes a model configured to calculate inner lithium-ion concentrations in the cathode and the anode based on a difference between diffusion of lithium ions in the cathode and the anode of the battery and to estimate the internal state of the battery based on the difference between the inner lithium-ion concentrations in the cathode and the anode.