Energy-Aware Vehicle Routing With User-Weighted Time Tradeoffs
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Solution Overview
Problem
Modern vehicles and navigation systems do not support real-time or near real-time routing options that maximize energy efficiency, despite advancements in vehicle technology, leading to suboptimal energy usage and increased emissions.
Innovation Solution
A system and method for generating navigation routes that consider real-time data and user preferences to balance travel time and energy efficiency, using models like linear regression, gradient boosting trees, and neural networks to compute optimal routes based on road network attributes and vehicle characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional routing methods are used, then travel time is minimized, but energy efficiency deteriorates
Solution Approach 1:
The routing system dynamically adjusts route selection based on real-time conditions including traffic patterns, terrain characteristics, and vehicle-specific energy consumption data. The system continuously updates routing decisions to balance time efficiency and energy efficiency, rather than using static predetermined routes
Solution Approach 2:
The system changes routing parameters by incorporating multiple optimization criteria (time, energy, terrain) and allowing users to adjust preference weights. The routing algorithm modifies path selection based on varying conditions such as traffic density, elevation changes, and vehicle performance characteristics
2Use of energy by moving object
If eco modes are selected, then energy efficiency is improved, but vehicle performance deteriorates
Solution Approach 1:
The system dynamically adjusts vehicle operating modes and routing decisions based on real-time conditions. Rather than permanently restricting performance in eco modes, the system selectively applies energy-efficient strategies only when routing decisions can compensate, maintaining performance when needed while optimizing energy use during suitable conditions
3Use of energy by moving object
If real-time routing data is collected and processed, then energy efficiency is improved, but system complexity increases
Solution Approach 1:
The system uses a multi-functional platform that combines routing optimization, energy consumption modeling, real-time data processing, and vehicle control functions. By integrating these diverse functions into a unified system, the patent reduces overall complexity compared to having separate specialized systems for each function
Solution Approach 2:
The system automatically collects, processes, and applies routing and energy data without requiring extensive manual intervention. The self-service approach includes automated data collection from vehicle sensors, automatic route optimization based on current conditions, and dynamic adjustment of routing decisions, reducing the operational complexity burden
Data Source
AI summary
A method for generating navigation routes for a vehicle includes: receiving an origin position, in a road network, of the vehicle; receiving a destination position, in the road network; receiving a user preference from a user interface device, the user preference indicating a tradeoff value having one of a plurality of values between travel time and energy efficiency; computing a time value for each of a plurality of road segments of the road network using a time consumption model; computing an energy consumption for each of the plurality of road segments of the road network using an energy consumption model; identifying a weighted route from the origin position to the destination position based on the time value and the energy consumption for the road segments and based on the user preference; and supplying the identified weighted route to a navigation system of the vehicle.