Battery SoC Estimation via Selective ECT Parameter Updating
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing battery state estimation methods, such as current integration and electrochemical thermal modeling, face challenges in maintaining accuracy due to battery cell state changes, leading to reduced estimation precision.
Innovation Solution
A battery optimization method that involves determining a state of charge (SoC) value, selectively adjusting parameters of an electrochemical thermal (ECT) model based on operating data, and updating these parameters to minimize optimization loss using Bayesian optimization, thereby maintaining accurate SoC estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If battery parameters are updated using traditional methods, then model accuracy deteriorates due to battery cell state changes, but updating all parameters increases computational burden
Solution Approach 1:
The patent segments the battery parameter update process by dividing parameters into two distinct groups: diffusion parameters (which change with battery cell state) and constant parameters (which remain stable). This segmentation allows selective updating of only the diffusion parameters based on current operating conditions, rather than recalculating all parameters. The segmentation resolves the contradiction by maintaining accuracy through targeted updates while reducing computational burden by excluding constant parameters from the update process.
Solution Approach 2:
The patent applies local quality by selectively updating parameters based on their specific characteristics and sensitivity to battery state changes. Diffusion parameters are identified as requiring frequent updates due to their direct relationship with battery cell state, while constant parameters are maintained at their initial values. This localized approach to parameter updating ensures accuracy is maintained where needed without unnecessarily complicating the overall system.
2Measurement precision
If full parameter re-estimation is performed, then estimation precision is maintained, but operational time and computational resources increase
Solution Approach 1:
The patent extracts and isolates the diffusion parameters from the complete parameter set, identifying them as the specific subset that requires updating. By taking out only the necessary parameters (diffusion parameters) and leaving the constant parameters unchanged, the system maintains estimation accuracy while significantly reducing the time and computational resources required for parameter updates compared to full parameter re-estimation.
Solution Approach 2:
The patent performs preliminary classification of parameters into diffusion and constant categories during model initialization. This preliminary action allows the system to pre-identify which parameters will require future updates, eliminating the need for time-consuming analysis during actual update operations. The pre-established parameter classification enables rapid, efficient updates while maintaining accuracy.
3Measurement precision
If Bayesian optimization is used to adjust parameters, then optimization loss is reduced, but computational complexity increases
Solution Approach 1:
The patent applies parameter changes by using Bayesian optimization to systematically adjust the diffusion parameters based on observed battery performance data. The optimization process modifies parameter values to minimize the difference between predicted and actual battery behavior, achieving high optimization accuracy. The complexity is managed by applying this sophisticated optimization method only to the reduced subset of diffusion parameters rather than the entire parameter set.
Data Source
AI summary
A battery optimization method and apparatus is provided. The battery optimization method includes selectively adjusting a parameter set related to a corresponding SoC section among a plurality of parameters of an electrochemical thermal (ECT) model based on operating data of each SoC section, and updating the plurality of parameters of the ECT model based on adjusted parameter sets.