Battery SOH Estimation Using Equivalent Circuit Parameters
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Solution Overview
Problem
Existing methods for estimating battery state of health (SOH) using Kalman filters suffer from high noise levels and inconsistent accuracy, which can lead to unreliable battery performance assessments.
Innovation Solution
Estimate SOH by calculating equivalent circuit model parameters such as R1C1, C1, R0, and R1, using a step change in current to determine these parameters, and apply predetermined relationships modeled as polynomial functions to improve accuracy and stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Kalman filter is used to estimate battery SOH, then SOH estimation can be obtained, but noise level is high and accuracy is inconsistent
Solution Approach 1:
The patent introduces an equivalent circuit model as an intermediary between the battery and the estimation algorithm. The model parameters (R0, R1, C1, R1C1) serve as mediators that capture the battery's dynamic behavior, allowing the Kalman filter to estimate SOH based on model parameter degradation rather than directly from noisy voltage-current measurements. This intermediary modeling approach filters out measurement noise while preserving the underlying degradation trends.
Solution Approach 2:
The patent transforms the SOH estimation problem from directly analyzing voltage-current measurements to monitoring changes in equivalent circuit model parameters over time. By tracking how parameters like R1, C1, and R1C1 change as the battery degrades, the system converts a noisy direct measurement problem into a parameter evolution analysis problem, improving both accuracy and reliability.
2Measurement precision
If multiple equivalent circuit model parameters are estimated and combined, then SOH estimation accuracy improves, but device complexity increases
Solution Approach 1:
The patent merges multiple equivalent circuit model parameters (R0, R1, C1, R1C1) into a unified SOH estimation through the Kalman filter framework. Instead of analyzing each parameter separately, the system combines their degradation information using predetermined relationships, achieving more accurate SOH estimation while managing complexity through integrated processing.
Solution Approach 2:
The equivalent circuit model serves multiple functions simultaneously: it characterizes the battery's electrical behavior, provides parameters for SOH estimation, and enables the Kalman filter to process measurements. This multi-functionality reduces overall system complexity by using a single modeling framework for multiple purposes.
Data Source
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AI summary
A method and apparatus for determining a state of health of a battery are disclosed. The apparatus comprises means (40) for sensing a voltage of the battery, means (42) for sensing a current through the battery, means (50, 52, 54, 56) for estimating a value of an equivalent circuit model parameter of the battery from the sensed voltage and current, wherein the equivalent circuit model parameter is based at least in part on an internal capacitance of the battery, means (66) for storing a predetermined relationship between the equivalent circuit model parameter and the battery state of health, and means (58, 60, 62, 64) for calculating a state of health of the battery based on the estimated value of the equivalent circuit model parameter using the predetermined relationship.