Battery Charge Monitoring via Kalman Filter Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for determining the state of charge and total energy storage capacity of batteries in vehicles, such as electric and hybrid vehicles, face challenges in accuracy due to estimation errors that accumulate over time, especially with infrequent calibration and are prone to noisy measurements and aging effects.
Innovation Solution
A method and device that utilize a combination of current integration and battery modeling, incorporating Kalman filter techniques to update charge estimates, account for measurement errors, and monitor battery health by integrating battery current and voltage data, allowing for accurate state of charge and capacity monitoring without the need for specific calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If current integration method is used to determine state of charge, then the method is easy to implement, but estimation errors accumulate over time
Solution Approach 1:
The patent implements feedback by using the battery model to predict terminal voltage and comparing it with actual measurements. The difference (residual) is used to update model parameters and correct the state of charge estimate, preventing error accumulation in the current integration method
Solution Approach 2:
The battery model acts as an intermediary between the current integration method and the terminal voltage measurements. It translates current measurements into predicted voltage, which is then compared with actual voltage to detect and correct integration errors
2Measurement precision
If battery model-based methods are used to determine state of charge, then measurement accuracy is improved, but device complexity increases
Solution Approach 1:
The patent uses parameter changes by adapting the battery model parameters (resistance, capacitance, open circuit voltage) to match the actual battery characteristics through online parameter estimation. This allows the model to remain simple while accurately representing the specific battery being monitored
Solution Approach 2:
The patent implements dynamic adaptation of the battery model parameters in real-time based on operating conditions. The model transitions from static to dynamic as parameters are continuously updated using measurement data, maintaining accuracy without requiring a complex fixed model
3Ease of manufacture
If off-load voltage measurement is used for calibration, then the method is simple, but calibration frequency is limited by rest period requirements
Solution Approach 1:
The patent performs preliminary action by continuously updating the battery model parameters during normal operation using a sliding window approach. This preparation ensures the model is already calibrated when calibration is needed, eliminating the need to wait for rest periods
4Measurement precision
If voltage analysis methods are used to monitor battery, then state of charge can be determined, but polarization effects adversely change indication accuracy
Solution Approach 1:
The patent uses dynamics by implementing a time-varying battery model that adapts to changing operating conditions including polarization states. The model parameters are continuously updated to reflect the current polarization level, allowing accurate state of charge determination despite polarization effects
Data Source
AI summary
A method and corresponding device, for monitoring a charge of a battery. The method obtains a battery current measurement value and a battery voltage measurement value, and applies a current integration method to update a primary charge estimate value representative of the charge stored in the battery by taking into account the battery current measurement value. The method further determines an ancillary charge estimate value representative of the charge stored in the battery using a battery model taking into account the battery voltage measurement value, and determines an error value for the ancillary charge estimate value, in which the error value expresses the reliability of the battery model. The method also applies a correction to the primary charge estimate value as function of the ancillary charge estimate value and the error value. Interpretation of the correction applied in this manner allows determination of current sensor offset and battery capacity change.