Li-Ion Battery SOC Estimation via Simplified First Principle Model
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Solution Overview
Problem
Traditional methods for estimating State of Charge (SOC) and State of Health (SOH) of Li-batteries face challenges in accuracy and computational efficiency, particularly for embedded applications, due to high computation requirements of electrochemical models and limited accuracy of equivalent circuit models.
Innovation Solution
A Lebesgue Sampling-based Extended Kalman Filter (LS-EKF) method is introduced, which integrates a simplified first principle (SFP) model with Lebesgue sampling to estimate SOC and SOH, reducing computational complexity while maintaining high fidelity and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If electrochemical model is used for SOC and SOH estimation, then measurement precision is improved, but device complexity increases due to high computation requirements
Solution Approach 1:
The electrochemical model is segmented into two parts: a simplified first principle model for routine SOC estimation that reduces computation, and a full electrochemical model for periodic calibration and SOH estimation. This segmentation allows the system to maintain accuracy while reducing overall computational burden during normal operation.
Solution Approach 2:
The patent changes the parameter representation by using a simplified first principle model with reduced state variables and parameters compared to the full electrochemical model. This parameter reduction maintains essential electrochemical behavior while significantly lowering computation requirements for embedded applications.
2Device complexity
If equivalent circuit model is used for SOC estimation, then device complexity is reduced, but measurement precision deteriorates due to inability to simulate battery behavior accurately
Solution Approach 1:
The simplified first principle model acts as an intermediary between the equivalent circuit model and the full electrochemical model. It incorporates essential electrochemical mechanisms (solid-phase diffusion, liquid-phase diffusion, reaction polarization, ohmic polarization) while maintaining computational efficiency, thereby bridging the gap between accuracy and complexity.
3Measurement precision
If full electrochemical model is used for real-time applications, then measurement precision is improved, but productivity decreases due to high computation requirements
Solution Approach 1:
The system dynamically adjusts the model complexity based on operational requirements. During normal real-time operation, the simplified first principle model is used for high efficiency. When periodic calibration is needed or SOH estimation is required, the full electrochemical model is activated. This dynamic adaptation optimizes both accuracy and computational efficiency throughout the battery lifecycle.
Data Source
AI summary
Described herein is a high fidelity SFP Li-battery model, which can describe the internal electrochemical reaction mechanism accurately, to estimate State of Health (SOH) and State of Charge (SOC) of Li-batteries.