Secondary Battery Micro-Short Detection Using Voltage Difference
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Solution Overview
Problem
Existing secondary battery systems lack accurate methods for detecting micro-short circuits and other abnormalities, leading to potential safety risks and inefficiencies in power storage devices, particularly in vehicles.
Innovation Solution
A system utilizing a Kalman filter and neural network to detect micro-short circuits by monitoring voltage differences and implementing feedback corrections, combined with a neural network for improved accuracy in abnormality detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional monitoring methods are used to detect battery abnormalities, then the device complexity is low, but the measurement precision and reliability are insufficient for detecting micro-short circuits
Solution Approach 1:
The patent segments the battery pack into multiple individual battery units, each equipped with its own monitoring circuit. This segmentation allows for precise detection of abnormalities in each cell while distributing the complexity across multiple simple units rather than requiring one complex centralized system. The monitoring circuit measures voltage, current, and temperature of each individual battery separately.
Solution Approach 2:
The patent implements feedback mechanisms where the monitoring circuit continuously measures battery parameters and compares them against threshold values. When abnormalities such as micro-short circuits are detected through voltage deviations or temperature changes, the system provides feedback signals to trigger warnings or protective actions. This feedback loop enables precise abnormality detection through iterative measurement and comparison.
2Reliability
If simple monitoring circuits are used, then the device complexity is low, but the reliability of detecting micro-short circuits and abnormalities is insufficient
Solution Approach 1:
The patent incorporates preliminary protective actions by pre-setting threshold values for voltage, current, and temperature parameters. The monitoring circuit is designed to automatically detect deviations from normal operating ranges and trigger protective measures before severe damage occurs. This preliminary action approach ensures reliable detection of micro-short circuits by preparing the system in advance with predefined safety criteria.
Solution Approach 2:
The patent introduces an intermediary microcomputer or control unit that mediates between the simple monitoring circuits and the final decision-making system. This intermediary processes data from multiple sensors, performs complex calculations to detect micro-short circuits, and generates appropriate warning signals. The intermediary layer enables reliable abnormality detection without requiring the entire system to be complex.
3Measurement precision
If comprehensive monitoring of all battery parameters is implemented, then the measurement precision improves, but the loss of energy increases due to continuous monitoring and processing
Solution Approach 1:
The patent implements periodic monitoring instead of continuous monitoring of all battery parameters. The monitoring circuit measures voltage, current, and temperature at predetermined intervals or under specific conditions (such as during charging or discharging phases). This periodic action reduces energy consumption while maintaining sufficient measurement precision by capturing critical data points when abnormalities are most likely to occur.
Solution Approach 2:
The patent applies partial monitoring by focusing measurement efforts on the most critical parameters and battery cells that are most likely to exhibit abnormalities. Rather than continuously monitoring all parameters with equal intensity, the system selectively monitors key indicators such as voltage deviations and temperature changes in high-risk areas, achieving adequate measurement precision with reduced energy expenditure.
Data Source
AI summary
A secondary battery control system that conducts abnormality detection while predicting other parameters (internal resistance, SOC, and the like) with high accuracy is provided. A difference between an observation value (voltage) at a certain point in time and a voltage that is estimated using a prior-state variable is sensed. A threshold voltage is set in advance, and from the voltage difference that is sensed, a sudden abnormality, specifically a micro-short circuit or the like is detected. Furthermore, it is preferable that detection be performed by using a neural network to learn data on voltage difference in a time series and determine abnormality or normality.