Single Particle Model for Lithium-Ion Battery SOC and SOH Estimation
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Solution Overview
Problem
Traditional lithium-ion battery state-of-charge (SOC) and state-of-health (SOH) estimation methods based on equivalent circuit models have low accuracy, which affects the safety and efficiency of lithium-ion battery systems, particularly in applications like electric vehicles and energy storage systems.
Innovation Solution
A method employing particle swarm optimization and a Lebesgue sampling-based Bayesian estimation framework to estimate SOC and SOH using a single particle model, which describes the behavior of lithium-ion batteries by solving the Spherical Diffusion Equation and Evolution of Lithium Concentration, reducing computational complexity and enabling deployment on embedded systems or microprocessors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional equivalent circuit models are used for SOC and SOH estimation, then the implementation is simple, but the estimation accuracy is low
Solution Approach 1:
The patent segments the complex battery system into a single representative particle model, focusing computational resources on the most critical aspects of lithium-ion transport while ignoring less significant spatial variations. This segmentation allows achieving high estimation accuracy with reduced model complexity compared to full 3D electrochemical models.
Solution Approach 2:
The patent transforms the physical battery parameters (particle radius, diffusion coefficient, concentration) into a simplified single-particle representation while maintaining the essential electrochemical relationships. This parameter transformation enables accurate SOC and SOH estimation without requiring complex multi-dimensional spatial discretization.
2Measurement precision
If complex electrochemical models are used to improve accuracy, then estimation precision improves, but computational cost increases
Solution Approach 1:
The patent extracts only the essential elements needed for accurate SOC and SOH estimation from complex electrochemical models, specifically retaining the single-particle diffusion dynamics while removing computationally intensive spatial discretization and thermal effects. This extraction achieves high accuracy with minimal computational cost.
Solution Approach 2:
The patent employs dynamic parameter adaptation where model parameters (diffusion coefficient, reaction rate) are updated in real-time based on operating conditions through particle swarm optimization. This dynamic approach maintains high estimation accuracy across varying battery states without requiring a fixed complex model structure.
3Reliability
If high-accuracy estimation methods are implemented, then reliability improves, but ease of operation deteriorates due to computational requirements
Solution Approach 1:
The patent creates a simplified computational copy of the battery's electrochemical behavior using a single-particle model that replicates the essential dynamics of lithium-ion transport. This copied model runs efficiently on embedded systems while maintaining sufficient accuracy for safety-critical SOC and SOH estimation, making the system easy to deploy without requiring high-performance computing resources.
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 SOC and SOH estimation, reducing computational costs and improving performance compared to traditional methods, enabling reliable operation of lithium-ion battery systems.
Implementation Method 1
The solid phase may be described via: Spherical Diffusion Equation—Fick Second Law
Implementation Method 2
the liquid phase may be described via: Evolution of lithium concentration
Data Source
AI summary
Described herein are methods of Lithium battery health management based on a single particle model as shown and described herein to provide for reliable and accurate battery factor estimation to ensure efficient system operation.