Battery Safety Estimation via Machine-Learned Voltage Behavior
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Solution Overview
Problem
Current techniques fail to effectively estimate safety regarding heat generation in batteries, particularly for batteries with unknown designs, due to limitations in measuring temperature behavior and its correlation with gas generation rates and voltage behavior.
Innovation Solution
A safety estimation device that acquires design parameters of batteries and uses a machine-learned logical model to calculate voltage behavior, which is then used to estimate safety regarding heat generation, incorporating gradient boosting for precise estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If temperature behavior is measured to estimate safety regarding heat generation, then safety information can be obtained, but measurement precision is insufficient due to low temporal resolution and weak correlation with gas generation rates
Solution Approach 1:
The patent introduces voltage behavior as an intermediary parameter that strongly correlates with both temperature behavior and gas generation rates. Instead of directly measuring temperature (which has low temporal resolution), the system measures voltage (high temporal resolution) and uses it to infer safety-related information about heat generation and gas production through established correlations.
Solution Approach 2:
The patent replaces direct thermal measurement (mechanical/physical temperature sensing) with electrical measurement (voltage monitoring). Voltage behavior serves as an electrical proxy for thermal and chemical states, enabling safer, more precise, and higher-resolution safety assessment without direct contact with hot or chemically active components.
2Adaptability or versatility
If design parameters are used to estimate safety for unknown battery designs, then versatility is improved, but measurement precision deteriorates without specific battery data
Solution Approach 1:
The patent transforms the estimation approach by changing from direct physical measurement to parameter-based calculation. By using design parameters (material compositions, electrode structures, electrolyte properties) as inputs to calculation formulas, the system adapts to different battery designs while maintaining precision through physics-based relationships rather than empirical calibration for each specific battery.
Solution Approach 2:
The patent creates a universal safety estimation system that works across different battery designs by using fundamental design parameters that are common to all non-aqueous electrolyte secondary batteries. The calculation formulas incorporate universal relationships between design parameters, voltage behavior, and safety metrics, making the system applicable to unknown or varied battery configurations.
Data Source
AI summary
A safety estimation device for batteries includes a parameter acquirer that acquires a design parameter of a battery, a calculator that calculates voltage behavior of the battery from the design parameter, based on a machine-learned logical model, and an outputter that outputs the voltage behavior as information about safety regarding heat generation of the battery.