Battery SOC Estimation with Sensor Fault Compensation
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Solution Overview
Problem
Existing methods for estimating the state of charge (SOC) of battery cells in electric vehicles face accuracy issues due to sensor faults such as bias and drift, which degrade the precision of SOC estimation.
Innovation Solution
A method that measures output current and voltage from the battery cell, incorporates fault estimates for current and voltage sensor errors into a SOC estimation model, and optimizes the SOC estimation using a Kalman filter to minimize the impact of these faults, thereby improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a Kalman filter based SOC estimation model is used, then the precision of SOC estimation is improved, but the accuracy is degraded due to sensor faults such as bias and drift
Solution Approach 1:
The patent introduces an intermediary compensation mechanism that mediates between the faulty sensor measurements and the SOC estimation model. By adding fault compensation terms (bias and drift compensation) as intermediate variables, the system can correct the distorted measurements before they affect the SOC calculation, thus maintaining both precision and accuracy
Solution Approach 2:
The patent changes the parameters of the estimation model by adding fault-related parameters (bias compensation parameter and drift compensation parameter) to the traditional Kalman filter model. This parameter expansion allows the system to account for sensor faults dynamically, transforming the model from one that assumes perfect sensors to one that actively compensates for sensor degradation
2Device complexity
If sensor measurements are used directly in the SOC estimation model, then the estimation process is simple, but the accuracy degrades due to bias and drift errors
Solution Approach 1:
The patent applies preliminary action by performing fault compensation before the main SOC estimation process. The bias and drift compensation calculations are executed in advance to correct the sensor measurements, so that the corrected values are then fed into the SOC estimation model. This preliminary correction step prevents fault propagation while maintaining overall process efficiency
Solution Approach 2:
The patent segments the SOC estimation process into distinct functional modules: fault detection, bias compensation, drift compensation, and final SOC calculation. This segmentation allows each module to handle specific aspects of the estimation task independently, making the complex process more manageable and maintainable while improving overall accuracy
Data Source
AI summary
The invention relates to a method for robust estimation of state of charge (SOC) for a battery cell (6) for an electric vehicle, the method comprising: measuring an output current (/m) from the battery cell; a temperature (Tm) of the battery cell; and an output voltage (y) from the battery cell; providing a SOC estimation model (M) for the battery cell comprising the measured current (/m) and the measured temperature (Tm) to provide an estimated output voltage (y); calculating the estimated output voltage (y) and an intermediate SOC value (SOCint) using the SOC estimation model (M); calculating a voltage difference between the estimated output voltage (y) and the measured voltage (y); estimating the SOC (SOC) for a battery cell by optimizing said SOC estimation model (M) based on the calculated voltage difference and the intermediate SOC value (SOCint). The method is characterized in that the SOC estimation model (M) further comprises a current fault estimate (lf) for an error of the measured current (/m); and/or the SOC estimation model (M) further comprises a voltage fault estimate (yf) for an error of a measured output voltage (ym); and in that the step of estimating the SOC (SOC) for a battery cell is further optimized based on the current fault estimate (lf) and/or the voltage fault estimate {yf). The invention further relates to a computer program comprising program code performing the steps of the method, a computer readable medium carrying such a computer program, a control unit (2) for controlling the monitoring the state of a battery cell, a battery state monitoring system, and an electrical vehicle comprising such a battery state monitoring system.