Battery Cell Internal Resistance Estimation Under Measurement Noise
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Solution Overview
Problem
Existing methods for estimating the internal resistance of a battery cell are hindered by measurement noise, disparities during charging and discharging, RC circuit-like behavior, and the inability of equivalent circuit models to perfectly map electrochemical processes, making accurate estimation difficult.
Innovation Solution
A method and device using differentiating filters to determine voltage and current differentials, calculate a correction factor, and iteratively update the internal resistance estimate based on a reference value, employing antisymmetric filters and potentially Kalman filters to reduce noise and improve signal-to-noise ratio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If voltage and current are used to estimate internal resistance, then the internal resistance can be tracked over time, but measurement noise and modeling inaccuracies reduce the estimation precision
Solution Approach 1:
The patent implements a feedback mechanism where the estimated internal resistance is continuously refined by comparing the modeled voltage differential (from the equivalent circuit model) with the actually detected voltage differential. The difference between these values feeds back into the estimation process, allowing the system to learn and correct modeling inaccuracies over time, thereby improving precision while maintaining reliable tracking
Solution Approach 2:
The patent introduces the voltage differential as an intermediary quantity that mediates between the raw voltage/current measurements and the internal resistance estimation. By working with differentials rather than absolute values, the system reduces the impact of measurement noise and improves the precision of the internal resistance estimation while maintaining the ability to track aging
2Ease of operation
If an equivalent circuit model is used to model the battery, then the internal resistance can be estimated, but the model cannot perfectly map electrochemical processes leading to estimation errors
Solution Approach 1:
The system uses feedback to continuously refine the equivalent circuit model parameters by comparing modeled voltage differentials with detected voltage differentials. This allows the simple equivalent circuit model to progressively adapt and improve its accuracy in mapping electrochemical processes without requiring a complex model structure
Solution Approach 2:
The patent dynamically adjusts the internal resistance parameter based on the difference between modeled and detected voltage differentials. By allowing parameters to change and adapt based on actual measurements, the equivalent circuit model achieves better mapping accuracy for electrochemical processes while maintaining its computational simplicity
3Measurement precision
If the internal resistance is determined as a differential internal resistance using filters, then measurement noise is reduced, but the complexity of the determination process increases
Solution Approach 1:
The patent segments the voltage signal processing into distinct stages: first detecting the voltage, then computing the differential through filtering, and finally using this differential for internal resistance estimation. This segmentation allows the use of filters to reduce measurement noise while keeping each processing stage relatively simple and manageable
Data Source
AI summary
Technologies and techniques for estimating an internal resistance of a battery cell. A voltage and a current are detected at the battery cell. A detected voltage differential is determined by differentiating the detected voltage using a filter, and a modelled voltage differential is determined from the detected current and an actual internal resistance estimation value is determined by differentiating using a filter. A correction factor is determined from the detected voltage differential and the modelled voltage differential, and a new actual internal resistance estimation value is estimated from the determined correction factor and the previous actual internal resistance estimation value, and the new actual internal resistance estimation value is provided as estimated internal resistance of the battery cell. A device configured for estimating an internal resistance of a battery cell is further disclosed.