Battery Short Resistance Estimation for Early Soft-Short Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods struggle to accurately detect persistent short circuits in lithium-ion batteries at early stages, leading to potential thermal runaway and device damage, with limited accuracy in detecting soft shorts and high inaccuracy at early life stages.
Innovation Solution
A processor-implemented method that monitors DC resistance values during charging or discharging, compares them to reference values, and uses an electrochemical-thermal model to estimate short resistance, detecting persistent short circuits and displaying alerts when thresholds are exceeded.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional detection methods are used to identify short circuits, then detection is only possible at later stages with limited accuracy, but early stage detection with high accuracy is required to prevent thermal runaway
Solution Approach 1:
The system performs preliminary monitoring of DC resistance values during normal charging/discharging operations before a short circuit fully develops. By establishing baseline resistance values and detecting deviations early in the degradation process, the system identifies potential short circuits at their initial stages rather than waiting for significant failures to occur.
Solution Approach 2:
The patent replaces conventional mechanical or electrical detection methods with an electrochemical-thermal model that uses computational analysis of DC resistance variations. This substitution enables more sensitive detection of early-stage short circuits by analyzing subtle changes in resistance patterns that conventional methods cannot detect.
2Reliability
If existing detection techniques are applied, then only persistent short circuits at later stages can be detected with 85% accuracy, but soft shorts and early stage short circuits cannot be reliably detected
Solution Approach 1:
The system changes the detection parameter from looking for significant resistance drops (conventional method) to monitoring deviations in DC resistance patterns over time. By analyzing the temporal evolution of resistance values and comparing them against the electrochemical-thermal model predictions, the system can detect soft shorts and early-stage persistent short circuits that do not cause immediate large resistance changes.
Solution Approach 2:
The electrochemical-thermal model provides continuous feedback by comparing actual measured resistance values against predicted values based on battery state (temperature, charge level, age). This feedback mechanism enables the system to adaptively identify deviations indicating short circuits, improving reliability across different battery conditions and stages.
3Measurement precision
If monitoring is performed continuously to detect early short circuits, then detection accuracy improves, but system complexity and computational requirements increase
Solution Approach 1:
The DC resistance monitoring system serves multiple functions: it characterizes battery health, detects short circuits, and provides data for state-of-charge estimation. By reusing existing monitoring infrastructure for multiple purposes, the system achieves high detection accuracy without proportionally increasing complexity.
Solution Approach 2:
The electrochemical-thermal model uses readily available battery operating parameters (temperature, voltage, current, charge level) that are already measured during normal operation. The model self-calibrates using standard battery characteristics, eliminating the need for additional sensors or complex external calibration equipment.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method effectively detects persistent short circuits with high accuracy, including soft shorts, and provides timely alerts, reducing the risk of thermal runaway and device damage by monitoring DC resistance values and applying an electrochemical-thermal model for precise estimation.
Implementation Method 1
the battery may be designed to store chemical energy and convert the chemical energy into electrical energy
Implementation Method 2
a large amount of thermal energy may be released because of the high amount of current flowing between the positive terminal and the negative terminal
Implementation Method 3
estimating, in response to detecting the persistent short circuit in the battery, a short resistance of the battery based on a value of power supply in the constant power charging or discharging window and a magnitude of the determined deviation
Data Source
AI summary
A processor-implemented method includes monitoring a plurality of direct current (DC) resistance values of a battery for a predefined time period during one of a constant power charging or discharging window of the battery, determining the plurality of DC resistance values of the battery at a plurality of timestamps corresponding to the predefined time period, determining a deviation of the determined plurality of DC resistance values by comparing the determined plurality of DC resistance values with a plurality of predefined DC resistance values associated with a reference battery, detecting a persistent short circuit in the battery in response to the determined deviation of the determined plurality of DC resistance values being greater than a threshold value, and estimating, in response to detecting the persistent short circuit in the battery, a short resistance of the battery based on a value of power supply in the constant power charging or discharging window and a magnitude of the determined deviation of the determined plurality of DC resistance values with respect to the plurality of predefined DC resistance values.