Vehicle Battery Telemetry for Real-Time Failure Prediction
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Solution Overview
Problem
Existing methods for predicting component failure in vehicle fleets are unreliable due to variations in operating conditions and lack of real-time monitoring, leading to unnecessary maintenance costs and vehicle breakdowns.
Innovation Solution
A vehicular telemetry system that collects and analyzes operational parameters in real-time, using statistical analysis and contextual information to generate predictions of component failure or deterioration, accounting for vehicle-specific conditions and interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Mean Time Between Failure engineering data is used to predict component failure, then a prediction timeline is established, but the prediction is inaccurate due to variations in operating conditions and lack of real-time monitoring
Solution Approach 1:
The system continuously monitors component operational parameters in real-time and feeds this data back to update the degradation model. This feedback loop allows the system to adjust predictions based on actual component behavior rather than relying solely on static historical data, thereby improving both reliability and measurement precision of failure predictions.
Solution Approach 2:
The system performs preliminary analysis of operational parameters to detect early signs of component degradation before actual failure occurs. By analyzing trends in voltage, current, temperature, and other parameters, the system can predict potential failures in advance, allowing for proactive maintenance while the component is still functional.
2Ease of manufacture
If manufacturer's recommended maintenance schedule based on running total of mileage or operational time is applied, then maintenance timing is standardized, but unnecessary maintenance costs are incurred due to inability to accurately predict actual component status
Solution Approach 1:
The system transitions from static, time-based maintenance scheduling to dynamic, condition-based scheduling. By continuously monitoring component health parameters and updating degradation models in real-time, the system adapts maintenance schedules to actual component conditions rather than following fixed intervals, thereby eliminating unnecessary maintenance while maintaining component reliability.
Solution Approach 2:
The system changes the basis of maintenance scheduling from time/mileage parameters to component condition parameters. By monitoring operational parameters such as voltage, current, temperature, and degradation rates, the system determines maintenance needs based on actual component state rather than predetermined time intervals, optimizing maintenance timing and reducing costs.
3Ease of operation
If simple comparisons of current value with previous value are used, then the monitoring process is simple, but accurate prediction of component failure cannot be achieved
Solution Approach 1:
The system introduces a degradation model as an intermediary between raw operational parameter comparisons and failure predictions. This model processes simple parameter comparisons and transforms them into reliable failure predictions by incorporating knowledge of component degradation patterns, operating conditions, and failure modes, thereby maintaining operational simplicity while improving prediction reliability.
Data Source
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AI summary
Apparatus, device, methods and system relating to a vehicular telemetry environment for monitoring vehicle components and providing indications towards the condition of the vehicle components and providing optimal indications towards replacement or maintenance of vehicle components before vehicle component failure.