Battery State Estimation With Electrochemical Model Feedback
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Solution Overview
Problem
Existing battery state estimation methods using electrochemical models often suffer from inaccuracies due to errors in sensor data input, leading to inconsistencies between sensed and estimated voltages, which can accumulate and degrade the accuracy of state information estimation.
Innovation Solution
A method and apparatus that utilize a voltage difference between sensed and estimated voltages to determine a state variation, update the internal state of the electrochemical model by correcting ion concentration distributions, and estimate state information more accurately using an electrochemical model with feedback mechanisms to minimize voltage differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an electrochemical model is used to estimate battery state, then estimation accuracy is improved, but calculation complexity increases
Solution Approach 1:
The patent applies feedback by comparing the estimated voltage from the electrochemical model with the actual sensed voltage, calculating a voltage difference, and using this difference to correct the model's internal state. This feedback loop continuously refines the model's accuracy without requiring complete redesign of the complex electrochemical model, thus improving estimation accuracy while managing computational complexity through iterative refinement rather than complete model replacement.
Solution Approach 2:
The patent changes parameters by adjusting the internal state of the electrochemical model based on the voltage difference between estimated and sensed values. Specifically, the model parameters (such as ion concentration distributions) are modified to compensate for discrepancies, allowing the complex model to adapt to actual battery conditions without requiring complete recomputation, thereby improving accuracy while controlling calculation burden.
2Productivity
If sensor measurements are used for battery state estimation, then real-time data availability is improved, but measurement errors increase
Solution Approach 1:
The patent uses an intermediary approach by introducing a correction mechanism that acts as a mediator between the sensor measurements and the final state estimation. The voltage difference calculation and subsequent model parameter adjustment serve as intermediary steps that reconcile the noisy sensor data with the physical constraints of the battery model, thereby maintaining real-time capability while improving measurement accuracy through systematic error correction.
Solution Approach 2:
The patent replaces direct reliance on sensor measurements with a hybrid approach that uses an electrochemical model to supplement and correct sensor data. Instead of trusting sensor readings alone, the system substitutes a portion of the measurement process with a physics-based model that provides complementary information, reducing the impact of sensor errors while maintaining real-time estimation through computational modeling rather than additional physical sensors.
3Measurement precision
If electrochemical model parameters are updated frequently, then estimation accuracy is improved, but calculation burden increases
Solution Approach 1:
The patent applies partial action by updating only the specific parameters of the electrochemical model that are necessary to correct the voltage discrepancy, rather than completely recomputing the entire model state. The correction is applied selectively to the internal state parameters (such as ion concentration distributions) based on the voltage difference magnitude, achieving improved accuracy through targeted parameter adjustment rather than exhaustive model recalibration, thus reducing overall computational load.
Data Source
AI summary
A processor-implemented method with battery state estimation includes: determining a state variation of a battery using a voltage difference between a sensed voltage of the battery and an estimated voltage of the battery that is estimated by an electrochemical model corresponding to the battery; updating an internal state of the electrochemical model based on the determined state variation of the battery; and estimating state information of the battery based on the updated internal state of the electrochemical model.