EV Route Planning With Adaptive Energy-Distance Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current vehicle navigation systems do not effectively account for energy constraints, such as battery charge levels in electric vehicles, making it difficult for drivers to determine if they have enough energy to reach their destination and providing inadequate suggestions for charging waypoints.
Innovation Solution
A method that receives information about a vehicle's energy storage state, planned driving route, and predicted driver characteristics, allowing drivers to input changes in driving behavior, which recalculates energy-versus-distance measures and suggests or automatically adds charging waypoints based on the vehicle's state of charge, providing graphical representations of remaining energy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional navigation systems are used without energy constraint integration, then basic route guidance is provided, but drivers cannot accurately assess energy levels or plan routes to ensure reaching destination
Solution Approach 1:
The patent combines the navigation system with energy management functionality by integrating energy-versus-distance calculation into the existing route planning infrastructure. The system merges GPS location data, route information, and energy consumption modeling into a unified display that shows both geographic progress and energy status on the same interface, eliminating the need for separate energy monitoring devices.
Solution Approach 2:
The navigation system is enhanced to perform multiple functions: traditional route guidance, real-time energy consumption tracking, predictive energy-versus-distance calculation, and charging waypoint suggestion. This multi-functional approach allows a single system to address both navigation and energy management needs without requiring separate specialized systems.
2Measurement precision
If detailed energy-versus-distance measures are provided with multiple driver characteristics, then energy management accuracy is improved, but system complexity and computational requirements increase
Solution Approach 1:
The system pre-calculates energy consumption parameters and driver characteristic profiles before the trip begins. By establishing baseline energy consumption rates and predicted driver behaviors in advance, the system reduces real-time computational requirements while maintaining measurement precision during actual navigation.
Solution Approach 2:
The system continuously monitors actual energy consumption and compares it with predicted values, using this feedback to adjust and refine energy-versus-distance calculations. This iterative process improves measurement precision over time while adapting to actual driver behavior patterns without requiring complete recalculation of all parameters.
3Reliability
If charging waypoints are automatically added based on state of charge, then reliability of reaching destination is improved, but additional stopping time and trip duration increase
Solution Approach 1:
The system proactively identifies and suggests charging waypoints in advance based on projected energy consumption and current state of charge levels. By calculating optimal charging stops before the vehicle runs out of energy, the system ensures destination reachability while allowing drivers to plan charging interruptions ahead of time rather than reacting to emergency low-energy states.
Solution Approach 2:
The charging waypoint suggestions are dynamically adjusted based on real-time energy consumption patterns, current state of charge, and remaining distance to destination. The system can modify recommended charging stops as driving conditions change, offering flexible options that balance reliability with minimizing trip time extensions.
Data Source
AI summary
A method includes: determining, based, at least in part, on a predicted driver characteristic, a first energy-versus-distance measure for a planned driving route of a vehicle, the first energy-versus-distance measure determined using an energy model; presenting the first energy-versus-distance measure on a user interface associated with the vehicle; identifying an already-driven part of the planned driving route; determining, based on information associated with the already-driven part of the planned driving route, a model error associated with the energy model; determining a second energy-versus-distance measure by modifying the first energy-versus-distance measure to account for the model error; and presenting the second energy-versus-distance measure on the user interface associated with the vehicle.