Adaptive Battery SOC Estimation via Polarization Voltage Segmentation
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Solution Overview
Problem
Existing battery cell state-of-charge estimation methods often require complex algorithms for high accuracy, which consume significant processor resources, and fail to provide precise estimates when the battery is nearly fully charged or discharged, while also neglecting important factors like polarization voltages and hysteresis.
Innovation Solution
A method that uses a battery-voltage based approach with current correction, incorporating adaptive estimation of polarization voltages and hysteresis, along with temperature sensing to determine state-of-charge, resistance, and available energy, which is computationally simpler and more accurate than existing methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex algorithms are used for battery cell state-of-charge estimation, then accuracy is improved, but processor resource consumption increases
Solution Approach 1:
The patent segments the state-of-charge estimation process into distinct operational regions (high SOC, mid-range SOC, low SOC) and applies different estimation algorithms to each region. This segmentation allows the system to use simpler algorithms in regions where they are sufficient while reserving complex algorithms only for regions where they are truly needed, thereby reducing overall computational complexity while maintaining accuracy.
Solution Approach 2:
The patent implements a dynamic algorithm selection mechanism that adapts the estimation approach based on the current operational state of the battery. The system dynamically switches between different estimation methods depending on whether the battery is in high SOC, mid-range SOC, or low SOC conditions, optimizing the balance between accuracy and computational resources in real-time.
2Measurement precision
If complex algorithms are used for battery cell state-of-charge estimation, then accuracy at all operational points is improved, but computational resources are consumed excessively
Solution Approach 1:
The patent applies different levels of estimation complexity to different operational regions of the battery. In the high SOC and low SOC regions where accuracy is most critical, the system applies more sophisticated estimation techniques. In the mid-range SOC region where extreme accuracy is less critical, simpler algorithms are used, thereby reducing overall processor resource consumption while maintaining accuracy where it matters most.
3Device complexity
If simpler algorithms are used for state-of-charge estimation, then computational complexity is reduced, but accuracy at high and low battery levels deteriorates
Solution Approach 1:
The patent implements a dynamic algorithm selection mechanism that adapts the estimation approach based on the current operational state of the battery. The system dynamically switches between different estimation methods depending on whether the battery is in high SOC, mid-range SOC, or low SOC conditions, optimizing the balance between accuracy and computational resources in real-time.
Data Source
AI summary
A system and method for determining an estimated battery cell state-of-charge and for determining an estimated battery cell resistance and for determining an estimated battery cell available energy is provided. The method includes measuring at least one of a battery cell voltage, a battery cell current, and a battery cell temperature. The method further includes determining an adapted estimated polarization voltage vector. The method further includes estimating the battery cell state-of-charge based on the adapted estimated polarization voltage vector and at least one of the battery cell voltage, the battery cell current, and the battery cell temperature. The method can further include computing the estimated battery cell resistance. The method further stores values corresponding to the estimated battery cell state-of-charge and the estimated battery cell resistance and the estimated battery cell available energy in a memory.