Electric Vehicle Fleet Management via Predictive Scoring
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Solution Overview
Problem
Conventional fleet management systems for electric vehicles lack efficient fuel management strategies, performance optimization, and maintenance recommendations, failing to consider long-term objectives and vehicle health monitoring.
Innovation Solution
A system comprising a processing subsystem hosted on a server that tracks vehicle data via sensors, generates unique profiles, computes fleet-level objectives, calculates scores, and implements predictive maintenance actions through a generic fleet action model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional fleet management systems are used to monitor electric vehicles, then basic vehicle data tracking is achieved, but the system fails to provide efficient fuel management, performance optimization, and maintenance recommendations
Solution Approach 1:
The system is divided into distinct functional modules: a tracking module for data collection, a scoring module for performance evaluation, and an action model for maintenance recommendations. Each module operates independently but contributes to the overall fleet management system, allowing complex functionality to be achieved through modular components that can be developed and maintained separately.
Solution Approach 2:
A fleet score is introduced as an intermediary metric that bridges raw vehicle data and maintenance decisions. The scoring module processes multiple vehicle parameters and transforms them into a comprehensive fleet score, which then guides the action model in generating maintenance recommendations. This intermediary simplifies the decision-making process by converting complex data into actionable insights.
2Loss of information
If comprehensive vehicle data tracking is implemented, then performance monitoring capability is improved, but the system cannot provide predictive maintenance and performance optimization
Solution Approach 1:
The system performs preliminary analysis by continuously calculating fleet scores based on tracked vehicle data. This ongoing evaluation prepares the system to predict maintenance needs and performance issues before they become critical problems. The action model uses historical scoring patterns to anticipate future requirements, enabling proactive rather than reactive fleet management.
Solution Approach 2:
The system establishes a feedback loop where vehicle performance data is continuously tracked, scored, and used to generate maintenance actions. The results of these actions are then fed back into the tracking system to refine future scoring and predictions. This closed-loop feedback mechanism enables the system to learn from past performance and improve its predictive capabilities over time.
Data Source
AI summary
A system for monitoring and maintaining a fleet of electric vehicles is disclosed. A processing subsystem includes an electric vehicle data tracking module which tracks vehicle data associated with each electric vehicle in the fleet of electric vehicles. An electric vehicle objective computation module utilizes one or more predefined fleet-level objectives corresponding to each of the electric vehicles in the fleet of the electric vehicles, ranks the one or more predefined fleet-level objectives in a predefined order. A fleet score computation module employs a predefined score model to calculate a vehicle level score corresponding to each of the one or more fleet level objectives ranked in predefined order, calculates a fleet level score, computes a fleet-level aggregate score for the fleet of the electric vehicles. A generic fleet action implementation module utilizes an implemented fleet action model for predicting maintenance and performance of the fleet of the electric vehicles.