Battery State Estimation Error Correction for SOC Accuracy
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Solution Overview
Problem
Existing state estimating devices for secondary batteries face challenges in accurately estimating open-circuit voltage characteristics and full charge capacity due to changes in battery state, particularly in hybrid and electric vehicles where non-load states are infrequent and short-lived, and current values fluctuate significantly.
Innovation Solution
A state estimating device that detects estimation errors in battery models and corrects open-circuit voltage characteristics by adjusting parameters based on charging rates, using a battery model that estimates open-circuit voltage and current values, and includes a full charge capacity estimating unit to calculate full charge capacity per unit plate area.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a battery model is used to estimate the state of a secondary battery, then the charging rate can be estimated during charging/discharging operations, but the estimation accuracy deteriorates over time due to changes in battery parameters such as internal resistance and full charge capacity
Solution Approach 1:
The system performs preliminary estimation of battery parameters (internal resistance, full charge capacity) during non-load states before actual charging/discharging operations begin. This preliminary action ensures that the battery model is updated with current parameter values before they are needed for accurate charging rate estimation, preventing accuracy deterioration over time.
Solution Approach 2:
The system continuously monitors actual battery behavior during charging/discharging and compares it with model predictions. When deviations are detected indicating parameter changes, the system feeds back this information to update the battery model parameters, thereby maintaining estimation accuracy over the battery's operational lifetime.
2Measurement precision
If the battery model parameters are updated frequently to maintain accuracy, then the estimation precision improves, but the computational complexity and processing time increase
Solution Approach 1:
The system updates battery model parameters periodically during non-load states rather than continuously during all operations. This periodic updating strategy maintains estimation accuracy by ensuring parameters are current, while reducing computational complexity by limiting updates to specific time windows when the battery is idle.
Solution Approach 2:
The system dynamically adjusts the timing and frequency of parameter updates based on battery state and operational conditions. During high-demand charging/discharging periods, updates are deferred to non-load states, while during stable periods, more frequent monitoring is performed. This dynamic approach balances accuracy requirements with computational resource availability.
Data Source
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AI summary
A battery state estimating unit (110) estimates an internal state of a secondary battery in accordance with a battery model equation in every arithmetic cycle, and estimates a charging rate (SOC) and a battery current based on a result of the estimation. A parameter estimating unit (130) obtains a battery current (Ib) measured by a sensor as well as the charging rate (SOC) and the battery current (Ite) estimated by the battery state estimating unit (110). The parameter estimating unit (130) estimates a capacity deterioration parameter such that a rate of change in difference (estimation error) between a summed value of an actual current and a summed value of an estimated current with respect to the charging rate (SOC) is minimized. A result obtained by estimating the capacity deterioration parameter is reflected in the battery model by the battery state estimating unit (110).