Battery Overpotential Modeling for Real-Time State Estimation
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Solution Overview
Problem
Existing lithium-ion battery management systems face challenges in accurately modeling and monitoring electrochemical overpotentials in real-time, particularly due to difficulties in obtaining frequency-domain data, which hinders effective state estimation and degradation analysis.
Innovation Solution
A proposed battery performance management framework using a discrete-time state-space approximation of the convolution-defined diffusion (CDD) model, specifically the receding-horizon diffusion (RHD) model, which characterizes ohmic, charge transfer, and diffusion overpotentials, allowing for real-time tracking of battery voltage and internal parameter monitoring, and determination of maximum power output capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physics-based models (PBM) are used for battery modeling, then measurement precision and insight into internal cell processes are improved, but device complexity and computational burden increase making real-time use difficult
Solution Approach 1:
The patent segments the complex physics-based model into a modular equivalent circuit model with distinct components: ohmic resistance, charge transfer RC pairs, and diffusion Warburg impedance. This segmentation allows the model to capture essential electrochemical processes while reducing computational complexity for real-time implementation.
Solution Approach 2:
The patent introduces an intermediary equivalent circuit model that bridges the gap between simple ECMs and complex PBMs. This intermediate model incorporates electrochemical elements (RC pairs representing charge transfer and Warburg elements for diffusion) to provide physics-based accuracy without the full computational burden of detailed PBMs.
2Measurement precision
If fractional-order models (FOM) are used to quantify electrochemical overpotentials, then measurement precision for degradation analysis is improved, but difficulty of detecting and measuring frequency-domain data in real-time increases
Solution Approach 1:
The patent transitions from static frequency-domain FOM representations to a dynamic time-domain equivalent circuit model. The dynamic ECM with voltage-dependent RC time constants and Warburg impedance can adapt to changing battery conditions without requiring frequency-domain measurements, enabling real-time overpotential quantification.
Solution Approach 2:
The patent replaces the frequency-domain measurement approach (electrochemical impedance spectroscopy) with a time-domain equivalent circuit model that uses standard voltage and current measurements. This substitution eliminates the need for complex frequency-domain data acquisition while maintaining the ability to quantify electrochemical processes.
3Productivity
If equivalent circuit models (ECM) are used for battery management, then ease of operation and computational speed are improved, but measurement precision for internal cell process monitoring deteriorates
Solution Approach 1:
The patent creates a composite equivalent circuit model that combines simple ECM elements (resistors, capacitors) with electrochemical elements (RC pairs for charge transfer, Warburg elements for diffusion). This composite structure maintains the computational efficiency of ECMs while incorporating the physics-based precision needed for internal cell process monitoring.
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
The RHD model achieves 99% accuracy in tracking battery voltage and provides insights into battery degradation, enabling effective monitoring and management of lithium-ion batteries, suitable for integration into electric vehicles and electrical grids, with potential for broader applications.
Implementation Method 1
Diffusion dynamics are derived from the convolution-defined diffusion (CDD) model
Implementation Method 2
The CDD model representing the diffusion overpotential behavior of the lithium-ion battery as the product of a diffusion related constant AD, and a convolution of a unit impulse response, gz(t), with a time-dependent diffusion state amplitude, ξ, for the lithium-ion battery
Implementation Method 3
The CPE is defined by a fractional-order transfer function, which is shown to accurately capture the charge transfer and diffusion overpotentials
Data Source
AI summary
Disclosed are systems, methods, and other implementations, including a method for monitoring and managing battery performance that includes deriving a representation of diffusion overpotential behavior for a lithium-ion battery according to a discrete-time state-space approximation of a convolution-defined diffusion (CDD) model for the lithium-ion battery, and determining behavior of the lithium-ion battery according to the discrete-time state-space approximation of the convolution-defined diffusion (CDD) model for the lithium-ion battery.