Battery Monitoring via Kalman Filter Inference
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Solution Overview
Problem
Existing battery monitoring approaches face challenges in high power applications due to slow internal electrochemical diffusion phenomena and manufacturing variability, leading to conservative operation and limited battery usage potential.
Innovation Solution
The use of an inferential sensor based on a modified Kalman filter for models with uncertain parameters to determine internal battery states, such as remaining charge and state of health, allowing for accurate monitoring and utilization of batteries in high power applications despite manufacturing variability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional battery monitoring approaches are used, then the system is simple to implement, but the monitoring accuracy is insufficient due to manufacturing variability and slow diffusion phenomena
Solution Approach 1:
The patent introduces an inferential sensor as an intermediary component that uses a Kalman filter algorithm to estimate unmeasured battery internal states (such as state of charge, state of health) based on available measurements. This intermediary processing layer bridges the gap between simple measurements and accurate internal state determination, resolving the contradiction by providing high accuracy through computational mediation rather than direct complex sensing.
Solution Approach 2:
The patent replaces complex physical sensing mechanisms with a computational approach using Kalman filtering. Instead of using elaborate hardware to directly measure internal battery states, the system substitutes mechanical/physical measurement complexity with algorithmic processing of electrical measurements (voltage, current, temperature), achieving accurate monitoring through mathematical modeling rather than physical complexity.
2Reliability
If conservative battery operation is adopted, then the reliability is improved, but the productivity is reduced due to limited battery usage potential
Solution Approach 1:
The patent implements continuous feedback through the Kalman filter that monitors battery internal states and provides real-time information about actual battery health and capacity. This accurate feedback enables the battery management system to optimize charging/discharging rates and operational parameters dynamically, allowing the battery to operate closer to its true limits safely, thus increasing productivity while maintaining reliability through informed control decisions.
Solution Approach 2:
The patent transitions from static conservative operation limits to dynamic adaptive operation. The Kalman filter continuously updates estimates of battery internal states, allowing the system to adaptively adjust operational parameters based on actual battery condition. This dynamic approach enables the battery to operate at optimal performance levels when conditions permit while maintaining reliability, resolving the contradiction between conservative limits and usage potential.
3Adaptability or versatility
If standard Kalman filter is used, then the computational approach is straightforward, but it cannot handle uncertain parameters in battery models
Solution Approach 1:
The patent modifies the standard Kalman filter by changing its parameter handling approach. Instead of assuming fixed known parameters, the extended Kalman filter treats battery model parameters as uncertain and estimates them concurrently with state variables. This parameter change in the algorithm's fundamental approach enables it to handle manufacturing variability and model uncertainties while maintaining the core Kalman filter structure, balancing adaptability with manageable complexity.
Data Source
AI summary
Methods, systems, and devices for monitoring a battery are described herein. One method includes receiving a plurality of values, each value associated with a respective battery characteristic, and determining an internal state associated with the battery based, at least in part, on the plurality of values.