Reinforcement-Learning Battery Equivalent Circuits Beyond Fixed Templates
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Solution Overview
Problem
Existing methods for generating battery equivalent circuits face challenges in accurately reflecting the diverse physicochemical characteristics of batteries due to the need for predefined templates, which limits the ability to analyze various battery characteristics effectively.
Innovation Solution
A method and apparatus using reinforcement learning to automatically generate a battery equivalent circuit by receiving actual impedance data, performing actions through an agent, providing rewards based on error comparison, and repeatedly refining the circuit until predefined conditions are met, thereby determining an optimal circuit structure and element values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If predefined equivalent circuit templates are used, then the circuit generation process is simplified, but the ability to accurately reflect diverse battery physicochemical characteristics is limited
Solution Approach 1:
The patent applies dynamics by transitioning from static predefined templates to a dynamic reinforcement learning-based generation process. The equivalent circuit structure and parameters are dynamically adjusted through iterative training with battery impedance data, allowing the system to adapt to diverse battery characteristics while maintaining automated generation capabilities
Solution Approach 2:
The patent changes parameters by using reinforcement learning to optimize circuit element values based on actual battery impedance measurements. The system iteratively adjusts resistance, capacitance, and other circuit parameters to minimize the difference between measured and predicted impedance, thereby accurately reflecting diverse battery physicochemical characteristics without relying on fixed templates
2Measurement precision
If various electrochemical characteristics are measured to generate equivalent circuit, then accurate battery representation is achieved, but the process becomes inconvenient and complex
Solution Approach 1:
The patent extracts only the essential impedance characteristics from battery measurements rather than requiring comprehensive electrochemical testing. By focusing on impedance data acquisition and using reinforcement learning to derive the equivalent circuit, the system reduces measurement complexity while maintaining accurate battery representation
Solution Approach 2:
The patent substitutes complex manual measurement and analysis processes with an automated reinforcement learning system. The machine learning agent automatically processes impedance data, optimizes circuit parameters, and generates the equivalent circuit model, replacing the need for manual electrochemical characterization and simplifying the overall process
3Adaptability or versatility
If reinforcement learning is used to generate equivalent circuit, then predefined templates are not needed, but the computational process becomes more complex
Solution Approach 1:
The patent achieves universality by designing a reinforcement learning framework that can handle various battery types and configurations through a unified approach. The same learning agent and training methodology work across different battery chemistries and designs, providing adaptability without requiring separate template-based approaches for each battery type
Solution Approach 2:
The patent applies self-service by enabling the reinforcement learning agent to automatically optimize equivalent circuit parameters through self-directed learning from impedance data. The system autonomously iterates through training episodes, adjusts parameters, and improves its predictions without manual intervention, reducing the operational complexity despite the sophisticated underlying algorithms
Data Source
AI summary
Provided are a battery equivalent circuit generation method and apparatus. An equivalent circuit generation apparatus repeatedly performs an action process of generating at least one equivalent circuit through an agent of a reinforcement learning model and a process of providing a reward based on an error identified by comparing actual impedance data of a battery with predicted impedance data identified through the at least one equivalent circuit until predefined conditions are satisfied, and then outputs an equivalent circuit generated in an action with a largest reward as an equivalent circuit of the battery. The disclosure was supported by the Ministry of Trade, Industry and Energy (Project: P0018425, Project Number (NTIS): 1415187492).