Collaborative Vehicle Battery Health Prediction Model
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Solution Overview
Problem
Current methods for predicting vehicle health and maintenance are inefficient, often requiring extensive resource consumption and inaccurate predictions due to the lack of real-world usage simulation, leading to premature or delayed maintenance.
Innovation Solution
A collaborative vehicle health management system that collects data from similar vehicles to create and refine a predictive model using sensor readings, adjusting parameters through methods like least mean square error to accurately forecast component lifespan and maintenance needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional vehicle health prediction methods are used, then maintenance can be performed, but resource consumption is extensive and prediction accuracy is low
Solution Approach 1:
The patent combines health data from multiple similar vehicles to create a collaborative prediction model. By merging data from peer groups (vehicles with similar usage patterns, environments, and component configurations), the system achieves higher prediction accuracy while distributing the computational burden, thereby reducing individual resource consumption compared to building separate models for each vehicle.
Solution Approach 2:
The system creates a virtual replica of the target vehicle's health status by copying and analyzing data from similar vehicles in the peer group. This allows the prediction model to leverage real-world usage patterns from multiple sources without requiring extensive physical testing or simulation resources for each individual vehicle.
2Reliability
If maintenance is performed based on traditional predictions, then vehicle operation continues, but maintenance may be premature or delayed due to inaccurate predictions
Solution Approach 1:
The collaborative prediction model continuously incorporates real-time health data from multiple vehicles, comparing actual component degradation patterns against predictions. This feedback mechanism allows the system to refine prediction accuracy over time, enabling more precise maintenance scheduling that avoids both premature and delayed maintenance interventions.
Solution Approach 2:
By analyzing aggregated data from peer groups, the system performs preliminary assessments of potential failures before they occur. This allows maintenance to be scheduled proactively at optimal times based on actual degradation trends observed across similar vehicles, rather than relying on fixed schedules or reactive repairs.
Data Source
AI summary
A method includes collecting vehicle health data from a plurality of vehicles. A peer group is identified among the plurality of vehicles. The collected vehicle health data from the peer group into a collaborative vehicle health model, the collaborative vehicle health model being applicable to a current vehicle to predict a state of at least a component of the current vehicle.