Battery State Estimation With Switched Multi-Gain Observers
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Solution Overview
Problem
Existing methods for estimating internal battery states in electric energy storage systems are computationally expensive and inefficient, particularly when dealing with large battery packs, as they require extensive calculations for accurate state-of-charge estimation.
Innovation Solution
A computationally efficient method using a switched multi-gain non-linear state observer that pre-calculates static observer gains based on the open circuit voltage curve, allowing the observer to switch between two gains during operation, reducing online calculations and ensuring global stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional state estimation methods (EKF, UKF, Luenberger Observer) are used to achieve accurate battery state prediction, then measurement precision is improved, but computational cost increases significantly
Solution Approach 1:
The battery pack is divided into multiple battery units (cells, modules, or packs) and each unit is monitored independently. The state estimation is performed segmentally for each battery unit rather than treating the entire pack as a single complex system, reducing the overall computational burden while maintaining accuracy for each unit.
Solution Approach 2:
The invention changes the observer gain from dynamic (time-varying) to static (fixed). By determining the observer gain once based on the open circuit voltage curve characteristics rather than recalculating it continuously, the computational complexity is significantly reduced while the estimation accuracy is preserved through proper selection of the static gain value.
2Adaptability or versatility
If dynamic observer gains are used to improve estimation accuracy across varying battery conditions, then adaptability is improved, but computational burden increases
Solution Approach 1:
The observer gain is determined in advance (pre-calculated) based on the open circuit voltage curve characteristics of the battery. This preliminary determination eliminates the need for continuous recalculation during operation, significantly improving computational efficiency while maintaining adaptability through the use of multiple static gain values corresponding to different operating regions.
Solution Approach 2:
The invention transitions from dynamic parameter adjustment to static parameter selection. By using multiple pre-determined static observer gains corresponding to different battery state regions (based on open circuit voltage), the system maintains adaptability to varying conditions without the computational overhead of continuous dynamic adjustment.
Data Source
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AI summary
The invention relates to a method for estimating or predicting an internal battery state of at least one battery unit (203) within an electric energy storage system (201), such as in a vehicle (200), the method comprising: - obtaining operational data of the electric energy storage system relating to operating conditions of the electric energy storage system, - feeding the obtained operational data to a non-linear state observer adapted to estimate and/or predict the internal battery state of the at least one battery unit in a series of time steps, such that an observer error of the non-linear state observer converges towards zero, or towards a value close to zero, - based on at least the obtained operational data, estimating or predicting the internal battery state using the non-linear state observer. The non-linear state observer is a switched multi-gain observer switching between at least two different static observer gains, wherein the observer gain to be used is selected based on a predicted or estimated value of the internal battery state as determined by the nonlinear state observer.