Fleet Battery Life Prediction Synchronizes Maintenance
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Solution Overview
Problem
Existing methods for managing electric vehicle batteries struggle to minimize operational losses due to simultaneous battery replacement in fleets, as they do not effectively align battery replacement times with scheduled maintenance, leading to increased downtime and costs.
Innovation Solution
A vehicle operation management method that predicts the remaining life of each battery and synchronizes battery replacement with periodic inspection and maintenance times by adjusting operation distances based on battery health, thereby reducing the frequency of halting operations for maintenance and replacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If battery replacement is performed when batteries reach end of life, then battery reliability is maintained, but operational loss increases due to simultaneous replacements across multiple vehicles
Solution Approach 1:
The system performs preliminary actions by predicting battery remaining life before actual battery failure occurs. The management device calculates remaining life based on battery capacity degradation trends and proactively schedules replacements during periodic maintenance intervals, preventing sudden battery failures and enabling advance operational planning.
Solution Approach 2:
The system dynamically adjusts vehicle operation plans based on real-time battery state predictions. By continuously monitoring battery capacity and predicting remaining life, the management device flexibly reallocates vehicles to different tasks or schedules maintenance during low-demand periods, thereby reducing operational loss while maintaining reliability.
2Productivity
If battery replacement is delayed to reduce operational loss, then operational continuity is improved, but battery reliability deteriorates due to extended use beyond design life
Solution Approach 1:
The system implements continuous feedback by monitoring battery capacity degradation and comparing it against predicted trends. The management device receives battery state information from vehicles, calculates remaining life based on degradation patterns, and adjusts maintenance schedules accordingly, creating a closed-loop system that balances operational continuity with reliability.
Solution Approach 2:
The system changes the parameter of maintenance timing from fixed calendar-based schedules to dynamic schedules based on actual battery degradation rates. By calculating remaining life based on capacity degradation trends, the system optimizes the timing of battery replacements to coincide with periodic maintenance intervals, extending operational continuity without compromising reliability.
3Reliability
If periodic maintenance is performed according to fixed schedule, then maintenance reliability is ensured, but operational loss increases due to halting vehicles that may not need immediate maintenance
Solution Approach 1:
The system transforms the maintenance parameter from fixed time-based intervals to condition-based intervals determined by actual battery degradation rates. By calculating remaining life based on capacity degradation trends, the system schedules maintenance only when actually needed, reducing unnecessary downtime while ensuring maintenance reliability through predictive timing.
Data Source
AI summary
A maintenance time acquisition unit acquires a periodic inspection and maintenance time determined in advance for each of a plurality of electric vehicles, a battery remaining life prediction unit predicts a remaining life of each of storage batteries from a state of the storage battery of each of the plurality of electric vehicles, and an operation plan creation unit creates an operation plan of the plurality of electric vehicles based on the periodic inspection and maintenance time of each of the plurality of electric vehicles and the remaining life of the storage battery of each of the plurality of electric vehicles.


