Battery SOC Estimation Using Adaptive H∞ Filtering and RC Modeling
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Solution Overview
Problem
Conventional SOC estimation methods for lithium batteries suffer from data saturation and reduced accuracy due to nonlinearity, non-Gaussian noise, and uncertainty, leading to inadequate performance in battery management systems.
Innovation Solution
A SOC estimation method utilizing a variable forgetting factor recursive least squares (VFF-RLS) algorithm with finite memory and an adaptive unscented H∞ filtering algorithm (AFOUHF) is employed to identify parameters of a second-order RC equivalent circuit model, enhancing parameter identification and noise filtering for accurate SOC estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional least squares algorithm is used for parameter identification, then the algorithm is simple to implement, but data saturation occurs and accuracy is reduced when dealing with nonlinear systems over time
Solution Approach 1:
The patent applies the variable forgetting factor recursive least squares (VFF-RLS) algorithm which dynamically adjusts the forgetting factor based on the residual error between predicted and actual values. This dynamic adjustment allows the algorithm to adapt to changing system characteristics and nonlinearities over time, preventing data saturation while maintaining implementation feasibility through the recursive structure.
Solution Approach 2:
The patent changes the parameter of the forgetting factor from a fixed value to a variable that adapts based on system conditions. By modifying the forgetting factor dynamically, the algorithm can better handle nonlinear systems and prevent accuracy degradation over time, while the underlying least squares framework remains relatively simple to implement.
2Adaptability or versatility
If Kalman filtering algorithm is used for SOC estimation, then adaptability to uncertainty is improved, but nonlinearity and non-Gaussian noise reduce estimation accuracy over time
Solution Approach 1:
The patent employs the adaptive unscented H-infinity filtering algorithm which dynamically adjusts to non-Gaussian noise and nonlinearities. The algorithm adapts its noise covariance matrices based on residual analysis, allowing it to maintain accuracy in uncertain and nonlinear conditions where conventional Kalman filtering fails.
Solution Approach 2:
The patent implements feedback mechanisms where the estimation algorithm continuously monitors the difference between predicted and actual measurements, and uses this feedback to adjust noise covariance matrices and adaptation factors. This feedback loop enables the system to compensate for nonlinearity and non-Gaussian noise, maintaining accuracy over extended periods.
3Measurement precision
If finite memory is incorporated in VFF-RLS algorithm, then the influence of old data is eliminated and parameter identification precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the data processing by incorporating a finite memory window that selectively retains only the most recent N data points. This segmentation eliminates the influence of outdated data on parameter identification while maintaining the recursive efficiency of the VFF-RLS algorithm, with the added complexity being minimal and localized to the memory management structure.
Data Source
AI summary
A SOC estimation method and system with advanced parameter identification and integrating infinite algorithm for battery monitoring is disclosed. According to the dynamic and static characteristics of the battery, a second-order RC equivalent circuit model is established, wherein the state equation and output equation of the model are calculated. A variable forgetting factor recursive least squares algorithm incorporating finite memory is utilized for parameter identification. An adaptive unscented H infinite filter algorithm is combined with a second-order RC equivalent circuit model with known parameters to establish the SOC estimation model, which can accurately reflect the state information of the battery to accurately estimate the SOC value of the battery.