Battery State Estimation via Current Bias Compensation
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Solution Overview
Problem
Conventional battery management systems in electrified vehicles face inaccuracies in state of charge (SOC), state of health (SOH), state of power (SOP), and impedance estimation due to inaccurate current, voltage, and temperature measurements, primarily caused by sensor errors and numerical instability in estimation algorithms.
Innovation Solution
A method that involves receiving current, voltage, and temperature measurements, estimating current bias, adjusting measured current, estimating open circuit voltage (OCV), and calculating SOC using a transformed OCV, with the application of Kalman filters and recursive least squares algorithms to enhance estimation accuracy, and utilizing nominal parameters based on temperature and SOC to improve battery state estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional battery management systems use standard measurement and estimation methods, then the system structure is simple, but the measurement precision and estimation accuracy of SOC, SOH, SOP, and impedance are insufficient
Solution Approach 1:
The system performs preliminary calibration to determine sensor bias and noise characteristics before actual battery operation. This pre-characterization of sensor errors allows the estimation algorithm to compensate for inaccuracies, improving measurement precision without requiring more complex hardware
Solution Approach 2:
The system implements an iterative estimation algorithm that uses feedback from voltage measurements and equivalent circuit model predictions to continuously refine SOC, SOH, SOP, and impedance estimates. This feedback mechanism compensates for sensor inaccuracies and improves estimation accuracy over time
2Reliability
If the system implements comprehensive bias estimation and correction algorithms, then the estimation accuracy of battery states improves, but the computational complexity and processing time increase
Solution Approach 1:
The system pre-determines sensor bias and noise characteristics through calibration procedures before normal operation. This preliminary characterization simplifies the real-time estimation algorithm by providing known correction parameters, reducing computational complexity while maintaining high estimation accuracy
Solution Approach 2:
The system transforms the estimation problem by changing parameters from direct measurement to model-based estimation using equivalent circuit models. This parameter transformation allows the use of simpler algorithms that leverage the pre-characterized sensor properties to achieve accurate estimates
3Measurement precision
If the system uses sensor bias estimation and current correction, then the SOC estimation accuracy improves, but the processing time and computational load increase
Solution Approach 1:
The system performs bias estimation and noise characterization during low-current periods or calibration phases before full operation. This preliminary action allows the main estimation algorithm to use pre-computed correction parameters, reducing real-time processing time while maintaining high SOC estimation accuracy
Solution Approach 2:
The system applies bias correction selectively during critical estimation phases rather than continuously. By applying corrections only when most beneficial (e.g., during low-current periods or key estimation cycles), the system achieves improved accuracy without the full computational overhead of continuous correction
Data Source
AI summary
More accurate and robust battery state estimation (BSE) techniques for a battery system of an electrified vehicle include estimating a current bias or offset generated by a current sensor and then adjusting the measured current to compensate for the estimated current bias. The techniques obtain nominal parameters for a battery model of the battery system based on a measured temperature and an estimated open circuit voltage (OCV). The techniques use these nominal parameters and the corrected measured current to estimate the OCV, a capacity, and an impedance of the battery system. The techniques utilize the OCV to estimate a state of charge (SOC) of the battery system. The techniques also estimate a state of health (SOH) of the battery system based on its estimated capacity and impedance. The techniques then control the electrified vehicle based on the SOC and/or the SOH.