Battery State Estimation via FIR Filter and Kalman Update
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Solution Overview
Problem
Existing methods for real-time estimation of battery state of power (SOP) and state of charge (SOC) in battery-powered systems, such as electric vehicles and smartphones, are inadequate due to their non-linear behavior and the need for dynamic operational boundaries that account for aging and environmental factors, as traditional methods are either not applicable for online applications or overly conservative.
Innovation Solution
A method using a recursive algorithm that relates battery terminal voltage to current, incorporating open-circuit voltage and finite-impulse-response (FIR) filters to dynamically model kinetic voltage, allowing for real-time estimation of SOP and SOC through cycling with arbitrary or specified driving profiles, and adjusting parameters for stability and kinetic considerations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods such as hybrid pulse power characterization are used for SOP prediction, then measurement precision can be achieved in lab settings, but device complexity and applicability to on-line applications increase due to requirements for specific driving profiles
Solution Approach 1:
The patent extracts the essential mathematical model from complex lab-based hybrid pulse power characterization methods, isolating the core FIR filtering approach that can be implemented in on-line battery management systems without requiring complex measurement setups or specific driving profiles
Solution Approach 2:
The patent replaces complex physical measurement setups with a computational/mathematical model based on finite impulse response filtering, substituting physical lab equipment with algorithmic processing that achieves similar or better accuracy in on-line applications
2Reliability
If static current and voltage boundaries are used to protect the battery, then reliability is improved by preventing overcharge/discharge, but adaptability deteriorates because the boundaries do not reflect battery aging or changing conditions
Solution Approach 1:
The patent transforms static battery boundaries into dynamic, time-varying boundaries by continuously updating operational limits based on real-time battery state estimates (SOC, SOP) and aging characteristics, allowing the system to adapt to changing battery conditions while maintaining protection
Solution Approach 2:
The patent implements feedback mechanisms where battery state estimates and aging information continuously inform and adjust operational boundaries, creating a closed-loop system that adapts to aging and environmental factors while maintaining reliable battery protection
3Reliability
If conservative predetermined limits are used for battery operation, then reliability is improved by accommodating unpredictable factors, but productivity deteriorates because healthy batteries cannot operate beyond these limits
Solution Approach 1:
The patent dynamically changes operational parameters (current and voltage boundaries) based on real-time battery state estimates and aging characteristics, allowing the system to optimize power throughput for healthy batteries while maintaining safety through continuous monitoring and adaptive adjustment
Data Source
AI summary
Some variations provide a method for real-time estimation of state of charge and state of power of a battery, comprising: (a) cycling a battery with a driving profile; (b) utilizing a recursive algorithm that relates battery terminal voltage to battery current, wherein the algorithm includes open-circuit voltage and a finite-impulse-response filter to dynamically model kinetic voltage; measuring the battery terminal voltage and the battery current at least at a first time and a second time during cycling; calculating battery open-circuit voltage and finite-impulse-response filter parameters; calculating battery state of charge based on the open-circuit voltage; and calculating battery state of power based on the open-circuit voltage and the finite-impulse-response filter parameters. An extended Kalman filtering technique is incorporated for real-time updating of FIR model parameters. Only a single FIR filter is necessary, making these methods applicable for battery-powered systems with limited computing and storage capabilities.