Server Prognostics for Autonomous Vehicle Energy Efficiency
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Solution Overview
Problem
Autonomous vehicles face inefficiencies in energy consumption due to degrading components and systems over time, which reduces their range and increases the frequency of recharging, leading to higher demands on charging infrastructure and potential cost inefficiencies in replacing worn-out parts.
Innovation Solution
A server computing system that monitors and analyzes the energy efficiency of autonomous vehicles by comparing projected and actual power consumption, identifying degrading components, and recommending maintenance or replacement based on cost-benefit analysis, thereby optimizing energy use and extending vehicle range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If components and systems are used without monitoring their degradation, then the autonomous vehicle can operate continuously, but energy consumption increases and range decreases
Solution Approach 1:
The system performs preliminary monitoring and analysis of component degradation trends before significant energy efficiency losses occur. By continuously tracking power consumption data and comparing it against projected values, the system detects early signs of degradation and can schedule maintenance proactively, preventing the accumulation of energy losses that would otherwise reduce vehicle range and increase power consumption.
2Use of energy by moving object
If components are replaced frequently to maintain energy efficiency, then energy consumption is optimized, but maintenance costs and operational downtime increase
Solution Approach 1:
The system implements continuous feedback monitoring of power consumption data, comparing actual measurements against projected values to determine the true condition of components. This feedback mechanism enables condition-based maintenance, where components are replaced only when degradation actually impacts energy efficiency, rather than following fixed schedules. This approach minimizes unnecessary replacements and operational downtime while maintaining optimal energy consumption.
3Measurement precision
If comprehensive monitoring of all components is performed, then energy efficiency degradation is detected early, but system complexity and computational resources increase
Solution Approach 1:
The system extracts and monitors only the critical parameters and components that have the most significant impact on energy consumption. Rather than comprehensively monitoring all vehicle systems, the monitoring framework focuses on extracting key performance indicators from power consumption data that directly correlate with component degradation affecting energy efficiency. This selective approach maintains high detection accuracy while minimizing system complexity and computational requirements.
Data Source
AI summary
Described herein is a server computing system that controls an autonomous vehicle to perform an operation to at least one of measure or isolate an effect of a variable on an actual power consumption by the autonomous vehicle. Data indicative of the actual power consumption, which is generated based on the operation, and data indicative of a projected power consumption, which is accumulated based on prior execution of the operation by a same or different autonomous vehicle, is received by the server computing system to determine whether an energy efficiency of the autonomous vehicle is degraded. The operation may be performed to identify a degraded vehicle system or component of the autonomous vehicle or to identify an autonomous vehicle in a fleet of autonomous vehicles for which further analysis is desirable. An output is generated by the server computing system that is indicative of the energy efficiency prognostics.