EV Battery Fuse Life Prediction from Overcurrent Stress
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Solution Overview
Problem
The durability and performance of fuses in electric vehicle battery circuits degrade over time due to increased current flow, leading to potential blowouts even in normal operating conditions, and designing fuses to handle overcurrents increases costs without ensuring proper functionality.
Innovation Solution
A device and method using a sensor, processor, and memory to predict the life expectancy of fuses by generating and analyzing current information, measuring peak currents and times, and referencing a lookup table to provide fuse-life expectancy information and warnings when thresholds are exceeded.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the fuse is designed to superior specification to increase durability, then the fuse can handle higher currents, but costs increase significantly and the fuse may not blow out when needed
Solution Approach 1:
The system performs preliminary monitoring of current flow through the fuse and predicts remaining life before actual failure occurs. By using sensors to continuously measure current and a prediction algorithm to calculate remaining life based on accumulated stress, the system enables proactive replacement scheduling, preventing both premature failures and unnecessary replacements of still-functional fuses.
Solution Approach 2:
The system implements feedback by continuously monitoring current flow through the fuse and using this information to update the remaining life prediction. The sensor feeds real-time current data to the prediction algorithm, which adjusts the remaining life estimate based on actual operating conditions, enabling dynamic optimization of replacement timing and avoiding both premature and delayed replacements.
2Ease of manufacture
If the fuse operates in degraded performance state, then manufacturing costs are reduced, but the fuse may blow out in normal current regions interfering with vehicle operation
Solution Approach 1:
The system performs preliminary assessment of fuse health by continuously monitoring current flow and calculating remaining life before actual failure occurs. This allows the system to predict when a degraded fuse will fail and schedule replacement proactively, preventing unexpected blowouts during normal operation while allowing the fuse to operate in its degraded state until replacement is scheduled.
Solution Approach 2:
The system implements feedback monitoring of current flow through the fuse and uses this information to update remaining life predictions in real-time. This continuous feedback enables the system to detect degradation trends and predict failure before it occurs, allowing timely replacement that prevents operational interference while maintaining cost efficiency.
3Productivity
If fuse replacement is delayed to reduce maintenance costs, then operational expenses are reduced, but unexpected blowouts may occur causing vehicle downtime
Solution Approach 1:
The system performs preliminary prediction of fuse failure by continuously monitoring current flow and calculating remaining life. This enables proactive scheduling of fuse replacement during planned maintenance windows or convenient times, rather than waiting for actual failure. The prediction algorithm provides advance notice of impending failure, allowing operators to schedule replacements in advance and avoid unexpected vehicle downtime.
Solution Approach 2:
The system implements continuous feedback monitoring of fuse health through current flow measurement and remaining life calculation. This real-time feedback enables dynamic adjustment of replacement scheduling, allowing the system to optimize the timing of maintenance activities based on actual fuse condition rather than fixed intervals, thereby minimizing vehicle downtime while preventing unexpected failures.
4Reliability
If fuse replacement is performed too frequently to ensure safety, then reliability is maintained, but unnecessary replacement costs and resource waste increase
Solution Approach 1:
The system performs preliminary assessment of actual fuse condition and predicts remaining life before replacement is needed. This enables replacement to be scheduled at the optimal time - just before predicted failure - rather than using conservative fixed-interval replacement. By basing replacement decisions on actual predicted need rather than predetermined schedules, the system avoids premature replacement of still-functional fuses, reducing resource waste while maintaining safety.
Solution Approach 2:
The system implements feedback-based decision making by continuously monitoring current flow and updating remaining life predictions. This real-time information feedback enables replacement decisions to be based on actual fuse condition and predicted failure timing rather than conservative estimates, allowing optimization of replacement timing to match actual need and avoid unnecessary resource consumption.
Data Source
AI summary
A device for predicting a life expectancy of a fuse for a battery of an electric vehicle may include a sensor configured to generate and output current information about current flowing in the fuse, a processor, and a memory connected to the processor and configured to store a preset lookup table, the memory storing program instructions which are executable by the processor to generate fuse-life expectancy information corresponding to the fuse based on the lookup table and time corresponding to an excess when the current information exceeds a preset threshold value.