Dynamic Internal Impedance Estimation for Battery SOC Accuracy
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Solution Overview
Problem
The SOC estimation method using a Kalman filter for storage batteries faces accuracy issues due to the constant treatment of internal impedance, which changes with battery state, leading to suboptimal estimation of remaining stored power.
Innovation Solution
A device and method that updates the internal impedance of the equivalent circuit model based on influencing values such as temperature and SOC, allowing for more accurate state vector approximation and improved SOC estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the internal impedance of the storage battery is treated as a constant in the equivalent circuit model, then the device complexity is reduced and the estimation process is simplified, but the measurement precision of the SOC estimation deteriorates because the actual internal impedance changes with battery state
Solution Approach 1:
The patent applies the Dynamics principle by transitioning from a static equivalent circuit model with constant internal impedance to a dynamic model where internal impedance varies with battery state. The Kalman filter recursively updates the internal impedance values based on observed battery voltage, current, and temperature data, allowing the model to adapt to changing battery conditions during charge and discharge cycles, thereby improving SOC estimation accuracy without requiring manual recalibration.
Solution Approach 2:
The patent implements Parameter changes by allowing the internal impedance parameters (resistance and capacitance values) in the equivalent circuit model to change dynamically based on battery state of charge, temperature, and current conditions. The Kalman filter estimates these parameters recursively, adjusting them to match actual battery behavior, which resolves the contradiction between model simplicity and estimation accuracy.
2Measurement precision
If the internal impedance of the storage battery is changed dynamically in accordance with influencing values, then the measurement precision of SOC estimation is improved, but the device complexity increases due to the need for recursive parameter updates
Solution Approach 1:
The patent applies the Feedback principle through the Kalman filter, which continuously compares the estimated battery state with actual measurements and uses the difference (innovation) to recursively update the internal impedance parameters. This feedback mechanism allows the system to automatically adapt to changing battery conditions without external intervention, improving SOC estimation accuracy while keeping the complexity manageable through efficient recursive calculations.
Solution Approach 2:
The patent implements Self-service by enabling the estimation system to automatically adjust its own parameters (internal impedance values) based on observed battery behavior. The Kalman filter performs self-calibration by using measurement data to update the equivalent circuit parameters, eliminating the need for manual parameter tuning or external calibration equipment, thus improving accuracy without proportionally increasing operational complexity.
3Device complexity
If conventional SOC estimation methods (output voltage method, internal resistance method, current integration method) are used, then the device complexity is kept low, but the measurement precision of SOC estimation is insufficient compared to Kalman filter-based methods
Solution Approach 1:
The patent applies the Merging principle by combining multiple estimation approaches within the Kalman filter framework. It integrates the equivalent circuit model (which relates to voltage and current methods), capacity integration (related to current integration method), and parameter estimation into a unified recursive algorithm. This merging allows the system to leverage the strengths of multiple conventional methods while achieving superior accuracy through their coordinated operation.
Solution Approach 2:
The patent implementsUniversality by designing the Kalman filter-based estimation system to perform multiple functions simultaneously: it estimates SOC, updates internal impedance parameters, compensates for temperature effects, and provides state prediction. This multi-functional approach consolidates what would otherwise require separate estimation systems, achieving high precision without proportionally increasing device complexity.
Data Source
AI summary
This remaining stored power amount estimation device 3 includes: sensors 5, 6 for observing the state of the storage battery 2; and a remaining amount estimation unit 15 which, on the basis of a state vector xk representing the state of the storage battery 2 by a plurality of elements included in an equivalent circuit model 20 modeling the storage battery 2, and an observation vector yk representing an observed value based on the observation result, updates the state of the storage battery 2, using a Kalman filter, and estimates the SOC of the storage battery 2. The remaining amount estimation unit 15 changes the internal impedance of the storage battery 2 modeled by the equivalent circuit model 20, in accordance with values influencing the internal impedance.