Nonlinear RC ECM for Battery Dynamic Power Prediction
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Solution Overview
Problem
Existing battery modeling and management techniques struggle to accurately predict dynamic power due to battery nonlinearity, especially at low temperatures and varying current amplitudes, leading to potential under-prediction of available power and unnecessary device power throttling.
Innovation Solution
The implementation of a physics-based nonlinear fractional-order equivalent circuit model (ECM) that characterizes and compensates for battery nonlinearity by measuring current, voltage, and temperature, and using a linear model portion for current-independent physics and a nonlinear model portion for current-dependent physics, with online estimation and parameter modification to account for impedance changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a linear ECM is estimated online using low current amplitude during normal operation, then the model is simple and efficient to implement, but the model accuracy deteriorates when predicting power at high current amplitude due to battery nonlinearity
Solution Approach 1:
The patent applies dynamics by making the ECM adaptive and dynamic rather than static. The system continuously estimates ECM parameters online and adjusts them based on operating conditions, particularly temperature and current amplitude. This allows the model to transition from a simple linear approximation to a more accurate representation of battery behavior under different operating conditions, resolving the contradiction between model simplicity and prediction accuracy.
Solution Approach 2:
The patent changes the parameters of the ECM based on operating conditions. Specifically, it adjusts the ECM parameters (such as resistance and capacitance values) according to temperature and current amplitude. By modifying these parameters dynamically, the model can accurately represent both low-current and high-current behavior, eliminating the need for separate models and maintaining both simplicity and accuracy.
2Productivity
If battery nonlinearity is ignored and a linear ECM is used, then the model is computationally efficient, but the available power is under-predicted leading to unnecessary device power throttling
Solution Approach 1:
The system implements a dynamic ECM that adapts to changing operating conditions through continuous online estimation. This dynamic approach allows the model to maintain high computational efficiency while accurately capturing nonlinear battery behavior, thus preventing power under-prediction and avoiding unnecessary throttling while preserving computational efficiency.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors battery voltage, current, and temperature, and uses this information to update the ECM parameters in real-time. This feedback loop ensures that the model remains accurate under varying conditions without requiring complex offline characterization, maintaining both computational efficiency and prediction reliability.
3Measurement precision
If offline ECM characterization is performed at multiple current amplitudes to account for nonlinearity, then the power prediction accuracy improves, but the characterization process becomes complex and time-consuming
Solution Approach 1:
The patent extracts the essential nonlinear behavior of the battery by focusing on key parameters that dominate the nonlinear response, such as charge transfer resistance and diffusion impedance. By isolating and modeling these critical parameters separately, the system achieves accurate power prediction without requiring full characterization at multiple current amplitudes, thus reducing characterization complexity while maintaining accuracy.
Solution Approach 2:
Instead of performing complex offline characterization at multiple current amplitudes, the patent uses online parameter estimation that adapts to current amplitude changes in real-time. This approach replaces static multi-point characterization with dynamic parameter adjustment, achieving the same accuracy benefit with significantly reduced complexity and time requirements.
4Ease of operation
If the ECM parameters are fixed based on low current amplitude characterization, then the model is simple to implement, but it fails to capture impedance changes at high current amplitudes
Solution Approach 1:
The patent transforms the static ECM into a dynamic model where parameters are continuously updated based on operating conditions. This dynamic implementation maintains ease of use through automated online estimation while achieving high adaptability to different current amplitudes, as the model automatically adjusts its parameters to match the current operating regime without requiring manual reconfiguration.
Solution Approach 2:
The patent creates a universal ECM that can handle multiple operating conditions (different current amplitudes, temperatures) through a single unified model structure. By using online parameter estimation and temperature compensation, the same model implementation serves multiple functions across different operating regimes, maintaining simplicity while achieving broad adaptability.
Data Source
AI summary
A method for battery power management based on battery nonlinearity characterization and compensation using a nonlinear resistor-capacitor (RC) equivalent circuit model (ECM) may include: measuring a current, a terminal voltage, and a temperature of a battery; implementing the nonlinear RC ECM comprising fixed, logarithmically spaced time constants and current dependent resistances and capacitances; constraining resistances of the nonlinear RC ECM to be nonnegative and performing a nonlinearity characterization procedure using the nonlinear RC ECM configured to characterize variations of a battery impedance with current amplitudes over a range of frequencies and create ECM parameter variations with current amplitudes by fitting impedance variations by nonlinear RC ECMs; performing online estimation of the nonlinear RC ECM using a battery current and a terminal voltage of the battery over time; and modifying the online estimated nonlinear RC ECM using ECM parameter variations to compensate for nonlinearity of the battery.