Battery Risk Assessment Using Diffusion Resistance Deviation
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Solution Overview
Problem
In energy storage systems and electric vehicles using lithium batteries, unexpected fire accidents can occur due to undetected battery defects, leading to recalls and potential fires even after disconnecting the electrical circuit, highlighting the need for a battery management system that can detect signs of fire before severe damage happens.
Innovation Solution
A battery risk assessment device and method that monitors battery modules in real-time by generating sensing data on cell voltage, temperature, and current, calculates diffusion resistance data using a Thevenin equivalent circuit, and determines risk states based on deviations in voltage, temperature, and diffusion resistance, enabling early detection of potential fires.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If electrical circuit separation is performed by detecting sharp changes in voltage, current, or temperature signals, then the battery can be isolated from the electrical circuit, but the battery may already be severely damaged and fire may still occur
Solution Approach 1:
The system performs preliminary actions by calculating diffusion resistance values before severe damage occurs. The BMS continuously monitors and calculates diffusion resistance, enabling early detection of battery defects before they lead to sharp signal changes or fire, thus preventing the need for emergency circuit separation
Solution Approach 2:
The system provides beforehand cushioning by establishing a baseline diffusion resistance value and continuously comparing current values against this baseline. This creates an early warning system that cushions against potential fire hazards by detecting deviations before they become critical, allowing preventive measures to be taken
2Productivity
If a battery management system monitors voltage, current, and temperature information, then battery usage efficiency can be improved, but the system cannot detect battery defects early enough to prevent fire
Solution Approach 1:
The system changes the monitoring parameter from conventional voltage, current, and temperature to diffusion resistance. By calculating diffusion resistance values based on electrical characteristics and comparing them against baseline values, the system enables early detection of battery defects while maintaining efficient battery management
Solution Approach 2:
The diffusion resistance calculation acts as an intermediary that bridges conventional electrical measurements and early defect detection. The BMS uses diffusion resistance as an intermediate parameter that reflects battery health status before severe abnormalities occur in voltage, current, or temperature signals
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
The solution effectively assesses battery risks in real-time, allowing for proactive management and preventing fires by identifying deviations that could lead to severe damage, thereby ensuring safer and more efficient battery operation.
Implementation Method 1
calculate diffusion resistance data of the plurality of battery cells by using a Thevenin equivalent circuit of the battery module
Data Source
AI summary
A battery risk assessment device for a battery module including a plurality of battery cells, including a data measurement unit configured to generate sensing data corresponding to the battery module, wherein the sensing data include cell voltage data comprising voltage information about the plurality of battery cells, cell temperature data comprising temperature information about the plurality of battery cells, module voltage data comprising information about a voltage output from the battery module, and module current data comprising information about a current output from the battery module; a deviation calculator configured to: calculate diffusion resistance data of the plurality of battery cells by using a Thevenin equivalent circuit of the battery module, and calculate deviation information based on the sensing data and the diffusion resistance data, wherein the deviation information indicates at least one from among a voltage deviation of the plurality of battery cells, a temperature deviation of the plurality of battery cells, and a diffusion resistance deviation of the plurality of battery cells; and a risk assessment unit configured to determine a battery risk state based on the deviation information.