Battery SOC Estimation Using Extended Kalman Filtering
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Solution Overview
Problem
Existing methods for estimating the state-of-charge (SOC) of batteries are inaccurate due to current sensor errors and require suspension of battery use for accurate measurement, leading to inefficiencies and safety risks.
Innovation Solution
A method using a G parameter and an extended Kalman filter to estimate SOC in real time by updating SOC values based on current and voltage measurements, minimizing the impact of current sensor errors and enabling continuous battery operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the Ah counting method is used to estimate SOC, then the estimation is simple to implement, but the accuracy deteriorates due to current sensor errors
Solution Approach 1:
The patent introduces an extended Kalman filter as an intermediary computational framework that processes current sensor measurements along with voltage and temperature data. This filter acts as a mediator between the simple Ah counting method and the need for high accuracy, combining multiple measurement sources to produce accurate SOC estimates without requiring complex battery models or additional sensors.
2Measurement precision
If the OCV-SOC relationship method is used, then the estimation is highly reliable, but the battery must be left in a zero current state for long periods
Solution Approach 1:
The patent transitions from the static OCV measurement approach (which requires the battery to be stationary at zero current) to a dynamic estimation approach using the extended Kalman filter. This dynamic method continuously processes voltage, current, and temperature measurements during battery operation, enabling real-time SOC estimation without requiring the battery to be idle or at equilibrium states.
3Measurement precision
If a physics-based model is used for SOC estimation, then the accuracy is improved, but the complexity and convergence problems increase
Solution Approach 1:
The patent extracts and utilizes only the essential elements needed for accurate SOC estimation (voltage, current, and temperature measurements processed through the extended Kalman filter) rather than implementing a complete physics-based model. This selective approach removes the computational burden and convergence issues of full physics-based models while retaining the accuracy benefits of considering multiple measurement parameters.
Data Source
AI summary
A method of estimating a state-of-charge (SOC) of a battery includes: setting an initial SOC value and an initial Kalman error covariance value; receiving an estimated G parameter value, a current current value, and a current voltage value of the battery; updating a current SOC value and a current Kalman error covariance value of the battery by inputting the estimated G parameter value, the current current value, and the current voltage value to an extended Kalman filter; and outputting the current SOC value.