Electrochemical Battery Model Parameterization via Clustering
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Solution Overview
Problem
Conventional methods for determining the state of health of batteries, particularly in electrical energy stores, are inaccurate, leading to unreliable predictions and high fluctuations due to their reliance on physical ageing models, which are not directly measurable and require costly sensor installations.
Innovation Solution
A method for parameterizing an electrochemical battery model by selecting similar operating feature points from a cluster of batteries using a clustering method, combining model parameters to obtain fused parameters that accurately determine the state of health, thereby improving the accuracy of battery health assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional physical ageing models are used to determine state of health, then the method is simple to implement, but the measurement precision is poor with model errors exceeding 5%
Solution Approach 1:
The patent combines data from multiple batteries into a clustered group, merging their operating feature points and model parameters. By fusing parameters from similar batteries (e.g., through averaging or weighted combination), the system achieves higher measurement precision for state of health determination while managing complexity through data aggregation rather than individual detailed modeling of each battery.
Solution Approach 2:
Instead of directly measuring difficult-to-obtain state of health parameters with complex sensors, the patent creates copies of relevant operating features from similar batteries within the cluster. By copying and fusing parameters from batteries with similar operating characteristics, the system obtains accurate state of health estimates without requiring complex direct measurement apparatus for each individual battery.
2Measurement precision
If direct measurement of state of health with sensors is implemented, then measurement precision improves, but device complexity and production costs increase significantly
Solution Approach 1:
The patent introduces an intermediary approach by using operating feature points (such as voltage, current, temperature characteristics) as mediators between direct physical measurement and state of health determination. Instead of installing complex sensors directly on each battery, the system uses readily measurable operating features as intermediaries to infer state of health through clustering and parameter fusion, thereby maintaining measurement precision while avoiding complex sensor installations.
Solution Approach 2:
The patent replaces the mechanical/sensor-based direct measurement system with a computational/data-based system. Instead of using physical sensors to directly measure state of health parameters, the system substitutes a data processing approach that clusters batteries, extracts operating features, and fuses parameters computationally, thereby eliminating the need for complex sensor installations while achieving comparable or superior measurement precision.
3Reliability
If individual battery modeling is used, then adaptability to specific battery conditions is high, but the reliability of predictions decreases due to high fluctuations
Solution Approach 1:
The patent merges individual battery models into clusters of similar batteries, combining their operating feature points and model parameters. By fusing parameters across multiple batteries in the cluster (e.g., through averaging or weighted combinations), the system reduces prediction fluctuations and improves reliability while maintaining adaptability to specific battery conditions through the clustering approach that groups batteries with similar characteristics.
Solution Approach 2:
The patent applies local quality by creating clusters of batteries with similar operating characteristics rather than using a single universal model or purely individual models. Each cluster represents a local group with specific qualities, allowing the system to adapt to local battery conditions while benefiting from the statistical reliability of aggregated data within each cluster, thereby balancing adaptability and prediction reliability.
Data Source
AI summary
A method for parameterizing an electrochemical battery model of a specific battery of a device, includes providing a current operating feature point of the specific battery and operating feature points of further batteries of a plurality of further devices for various evaluation periods. The operating feature points of the further batteries characterize an operation of a respective further battery within one or more of the evaluation periods based on several operating features. The several operating features are produced depending on characteristics of operating variables of the respective further battery. The method further includes selecting operating feature points of the further batteries that are similar to the current operating feature point of the specific battery, and providing model parameters of the electrochemical battery model that are associated with the current operating feature point and the selected operating feature points.