Battery SoP Estimation Using EIS-Based Hyper Model Calibration
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Solution Overview
Problem
Current battery management systems (BMS) underestimate the maximum power that can be drawn from or put back into a battery pack, using simplistic and conservative estimates, and traditional SoP estimation methods are time-consuming and impractical due to the need for lengthy DCIR measurement tests and recalibration after cell aging.
Innovation Solution
A hyper model is trained using electrochemical impedance spectroscopy (EIS) scans to predict battery state of power (SoP), with pretraining and recalibration based on EIS scans at various battery states, including temperature ranges, states of charge, and current load, utilizing a family of equivalent circuit models (ECMs) for accurate and efficient SoP estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional DCIR measurement tests are used for SoP estimation, then measurement precision is improved, but loss of time increases due to lengthy tests with long relaxation times
Solution Approach 1:
The patent replaces time-domain DCIR measurement methods with frequency-domain electrochemical impedance spectroscopy (EIS) measurements. EIS uses sinusoidal current excitations at multiple frequencies to extract impedance parameters, providing accurate SoP estimation without requiring long relaxation times. The frequency-domain approach captures battery dynamics more efficiently than sequential time-domain pulses.
Solution Approach 2:
The patent employs periodic sinusoidal current excitations at various frequencies during EIS scans instead of aperiodic current pulses. These periodic excitations allow the system to probe battery impedance characteristics across different frequency ranges, enabling accurate SoP estimation while minimizing measurement time through efficient frequency sweeping.
2Measurement precision
If DCIR measurement tests are performed to train ECM, then measurement precision is improved, but productivity decreases due to time-consuming recalibration after cell aging
Solution Approach 1:
The patent replaces time-domain DCIR-based ECM calibration with frequency-domain EIS-based calibration. The EIS measurements provide comprehensive impedance characteristics across multiple frequencies, enabling more accurate and efficient ECM parameter extraction. This substitution reduces the time and resources required for recalibration after cell aging.
Solution Approach 2:
The patent measures impedance parameters at multiple frequencies during EIS scans to capture the frequency-dependent behavior of the battery. By fitting ECM parameters to these multi-frequency impedance data, the system achieves accurate calibration that accounts for aging effects without requiring excessive measurement time or frequent recalibration cycles.
3Device complexity
If simplistic conservative estimates are used for maximum power, then device complexity is reduced, but power estimation accuracy deteriorates
Solution Approach 1:
The patent replaces simplistic algebraic power estimation (P = V × I) with physics-based EIS measurements and ECM-based modeling. The EIS-derived impedance parameters feed into the ECM to predict voltage responses under various load conditions, providing accurate maximum power estimation that accounts for battery dynamics, temperature effects, and aging without excessive computational complexity.
Solution Approach 2:
The patent performs EIS scans and ECM calibration in advance to establish accurate battery characteristics before operational use. The pre-trained ECM models enable real-time maximum power estimation with high accuracy, avoiding the need for complex real-time calculations while maintaining precision through previously extracted impedance parameters.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method provides a fast and practical way to estimate battery SoP, avoiding premature aging and thermal issues by using EIS measurements, enabling precise power tracking and dynamic recalibration, thus improving vehicle performance and user experience.
Implementation Method 1
performing electrochemical impedance spectroscopy (EIS) scans on a plurality of batteries similar to the vehicle battery under various states of the battery
Data Source
AI summary
A method is provided for pretraining a hyper model configured for use in predicting a state of power (SoP) of a vehicle battery. The method includes performing electrochemical impedance spectroscopy (EIS) scans on a plurality of batteries having a set of similar operating characteristics to the vehicle battery. The EIS scans are performed across various states of the vehicle battery. The method further includes fitting parameters of the hyper model by applying an optimization technique to results of the EIS scans. The hyper model includes a family of models that each define a voltage response of a respective cell from among a plurality of cells of the vehicle battery to a current profile over the various states of the vehicle battery.