Autonomous Vehicle Routing With Energy-Aware Motion Control
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Solution Overview
Problem
Conventional vehicle route planners do not effectively factor in fuel prices or energy efficiency, particularly for autonomous vehicles using alternative energy sources, leading to increased operational costs.
Innovation Solution
A system and method for autonomous vehicle control that utilizes external and internal sensors, mapping processes, and energy-optimized motion planning techniques to minimize energy consumption by considering factors like drafting characteristics, road conditions, and vehicle performance, combined with environmental data and a predefined energy consumption model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional route planners optimize for distance or time, then routing efficiency is improved, but fuel cost optimization deteriorates
Solution Approach 1:
The system changes the optimization parameter from simple distance or time to a composite energy cost parameter that incorporates fuel prices, vehicle-specific consumption rates, and real-time operating conditions. This allows the route planner to evaluate routes based on actual energy expenditure rather than abstract metrics.
Solution Approach 2:
The system implements feedback loops where actual fuel consumption data and energy usage measurements are continuously fed back into the route planning algorithm. This enables dynamic adjustment of routing decisions based on real-world performance data, improving both time efficiency and energy optimization over time.
2Use of energy by moving object
If route planners incorporate fuel prices and energy efficiency, then energy cost optimization is improved, but system complexity deteriorates
Solution Approach 1:
The system designs a multi-functional energy management platform that handles route planning, real-time monitoring, data analytics, and vehicle control functions within a unified architecture. This universal system reduces overall complexity by eliminating the need for separate systems for each function.
Solution Approach 2:
The system introduces an intermediary energy management layer between the route planner and vehicle controls. This mediator translates complex energy data into actionable routing decisions and coordinates between different vehicle systems, simplifying the overall control architecture.
3Adaptability or versatility
If autonomous vehicles use alternative energy sources, then energy source versatility is improved, but energy efficiency determination capability deteriorates
Solution Approach 1:
The system adapts its measurement and optimization parameters based on the specific energy source being used. For electric vehicles, it monitors battery charge/discharge cycles and electrical consumption; for hybrid vehicles, it tracks both fuel and electrical parameters; for conventional vehicles, it focuses on fuel consumption. This dynamic parameter adjustment maintains measurement precision across diverse energy sources.
Solution Approach 2:
The system creates a universal energy efficiency determination framework that can handle multiple energy source types through a common architecture. It uses energy-equivalent conversion methods to standardize measurements across different energy sources, enabling precise comparison and optimization regardless of the specific fuel or power source.
Data Source
AI summary
A system and method for autonomous vehicle control to minimize energy cost are disclosed. A particular embodiment includes: generating a plurality of potential routings and related vehicle motion control operations for an autonomous vehicle to cause the autonomous vehicle to transit from a current position to a desired destination; generating predicted energy consumption rates for each of the potential routings and related vehicle motion control operations using a vehicle energy consumption model; scoring each of the plurality of potential routings and related vehicle motion control operations based on the corresponding predicted energy consumption rates; selecting one of the plurality of potential routings and related vehicle motion control operations having a score within an acceptable range; and outputting a vehicle motion control output representing the selected one of the plurality of potential routings and related vehicle motion control operations.