Battery Hazard Detection Using ΔSOC Temperature Profiles
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Solution Overview
Problem
Existing battery monitoring techniques fail to detect potentially dangerous conditions in lithium-ion batteries, such as thermal runaway, due to reliance on battery temperature alone, neglecting the impact of internal resistance and aging, which can lead to hazardous situations like explosions or fires.
Innovation Solution
A battery monitor ASIC that integrates temperature and internal resistance monitoring, using delta-state-of-charge (ΔSOC) data to detect abnormal temperature increases and aging, employing a hazard detection algorithm to identify hazardous conditions before they escalate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If battery temperature monitoring is used as the main indicator, then the monitoring system is simple to implement, but it fails to detect potentially dangerous conditions in time
Solution Approach 1:
The patent combines temperature monitoring with internal resistance monitoring into a unified hazard detection system. The battery monitor ASIC integrates both sensing functions and merges their data through a hazard detection algorithm that considers both parameters together, enabling more reliable detection of thermal runaway conditions while accounting for battery aging effects.
Solution Approach 2:
The system performs preliminary detection of abnormal temperature increases by comparing real-time temperature data against expected temperature profiles stored in memory. The hazard detection algorithm proactively identifies dangerous conditions before they escalate to thermal runaway, allowing preventive action to be taken in advance.
2Reliability
If only temperature monitoring is implemented, then the monitoring cost is low, but hazardous conditions caused by thermal runaway cannot be detected in time
Solution Approach 1:
The patent merges temperature monitoring and internal resistance monitoring into a single integrated system. The battery monitor ASIC combines both sensing functions and processes their data together through a unified hazard detection algorithm, enabling reliable thermal runaway detection while accounting for battery aging without requiring separate independent systems.
3Measurement precision
If battery aging and internal resistance are considered, then detection accuracy improves, but the monitoring system becomes more complex
Solution Approach 1:
The system performs preliminary characterization of battery aging by storing expected temperature profiles in memory during normal operation. These pre-established profiles serve as reference data that account for battery aging effects, allowing the hazard detection algorithm to compare real-time measurements against age-appropriate expectations without requiring complex real-time aging models.
Solution Approach 2:
The hazard detection algorithm continuously compares real-time temperature and internal resistance measurements against expected profiles stored in memory. This feedback mechanism adjusts hazard assessment based on actual battery behavior versus predicted behavior, improving detection precision while maintaining manageable algorithmic complexity through iterative comparison rather than complex calculations.
Data Source
AI summary
A method, system, and integrated circuit are provided for monitoring a battery within a host device for abnormal conditions. A first temperature is determined for a battery at a first state-of-charge (SOC) of the battery during a charge action. A second temperature for the battery at a second SOC of the battery during the charge action is determined, and a current delta-SOC for the charge action is determined. A memory in the host device holding delta-SOC temperature data associated with a plurality of delta-SOCs is accessed. Based on this, a determination is made as to whether a battery temperature increase for the charge action at the current delta-SOC is abnormal, the delta-SOC temperature data including at least first data associated with a normal battery performance profile and second data associated with an abnormal battery performance profile.