Energy Storage Parameter Map Estimation via Correlation Model
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Solution Overview
Problem
Existing methods for estimating the state of charge and operation conditions of energy storage devices, such as batteries in electric and hybrid vehicles, face challenges in accurately incorporating battery parameters and aging factors, leading to inefficiencies in estimating available power and energy.
Innovation Solution
A method and system that utilize a parameter map updated using a correlation model, incorporating recursive processes like least squares or Kalman filters, to estimate and refine parameter values and variances across operating points, ensuring more accurate state of charge and operation condition assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If battery parameters and aging factors are incorporated into the estimation model, then the accuracy of state of charge estimation is improved, but the complexity of the model increases
Solution Approach 1:
The parameter map is divided into multiple discrete operating points, each with its own parameter values. This segmentation allows the complex estimation problem to be broken down into manageable discrete states that can be updated independently, reducing overall model complexity while maintaining accuracy.
Solution Approach 2:
The invention updates parameter values in the parameter map based on measured battery data and observed changes. By dynamically adjusting parameters such as capacity and resistance values at different operating points, the model adapts to battery aging without requiring complete model reconstruction, thus improving accuracy while controlling complexity.
2Measurement precision
If the parameter map is updated using correlation models for all operating points, then the estimation accuracy is improved, but the computational time and resources increase
Solution Approach 1:
The correlation model is applied selectively to update parameter values at operating points that are relevant to current battery conditions or show significant deviations. This partial application of the update mechanism reduces computational burden while maintaining estimation accuracy for the most critical parameters and operating conditions.
Solution Approach 2:
The system uses measured battery data to identify which parameter values deviate from expected values in the parameter map. This feedback mechanism triggers selective updates only for affected operating points or parameter types, rather than performing blanket updates across the entire parameter map, thus reducing computational time while maintaining accuracy.
Data Source
AI summary
A method and a system are disclosed for determining a parameter map used for estimating an operation condition for an energy storage device which may be an energy storage device. The method includes updating the parameter map. When updating the parameter map, a correlation model is implemented taking into account aging behavior of the energy storage device. The model comprises correlation between different parts of the parameter map which enables updating the parameter map at parts which is presently not at a current operating point. Based on the parameter map, an operation condition of the energy storage device may be determined.