Dynamic EV Routing for Charging Availability and Reservation Changes
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Solution Overview
Problem
Conventional methods for electric vehicle routing fail to consider uncertainty in waiting time, charging time, driving preference, driving habits, and vehicle state of charge, leading to inefficient route planning and sparse charging station availability along optimal traffic routes.
Innovation Solution
A dynamic route planning system integrated with an infotainment system, navigation system, battery management system, and processor that receives user preferences and road conditions to determine optimal routes with charging stations, updating routes based on power consumption and charging station availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional map application route planning is used to ensure better traffic route, then travel time is reduced, but charging station availability becomes sparse
Solution Approach 1:
The system dynamically adjusts the routing strategy by switching between shortest path algorithms and charging-aware algorithms based on real-time battery state of charge levels. When battery charge is sufficient, the system prioritizes shortest travel time routes; when charge drops below thresholds, it automatically incorporates charging stations into the route planning, ensuring both travel efficiency and charging reliability
Solution Approach 2:
The system performs preliminary identification of charging stations along potential routes before final route determination. By pre-mapping charging infrastructure and calculating distances to nearest charging stations, the system prepares charging contingency plans in advance, allowing seamless integration of charging stops when needed without significantly deviating from optimal travel routes
2Reliability
If frequent charging is performed to address limited battery power, then energy supply reliability is improved, but travel time increases
Solution Approach 1:
The system implements autonomous monitoring of battery state of charge and automatic generation of charging schedules without user intervention. The routing algorithm continuously calculates optimal charging intervals based on remaining battery power, destination distance, and available charging infrastructure, enabling the vehicle to service itself at strategically determined points while minimizing impact on overall travel time
Solution Approach 2:
The system dynamically modifies routing parameters including charging thresholds, charging duration estimates, and route deviation tolerances based on real-time conditions such as battery degradation rates, charging station availability, and traffic patterns. By adjusting these parameters adaptively, the system optimizes the balance between maintaining sufficient energy supply and minimizing time spent on charging activities
Data Source
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AI summary
Embodiments relate to a dynamic routing system, comprising a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise a first receiving component that receives information of a trip comprising a destination and departure information, and an optimal routing component that determines an optimal routing based on current conditions, user preferences for charging stations, primary user's driving habits, battery's state of health, financial impact, and availability of charging stations at the time of receiving the trip information. At the time of trip set up, the system can make reservations for charging at the charging station requiring a reservation. During the trip, if the system requires a change to the reservation, the system establishes communication with the charging station and adjusts the reservation.