Electric Vehicle Power Management Using Itinerary Data
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Solution Overview
Problem
The limited availability of remote charging stations compared to gas stations leads to inconvenience and range anxiety for electric vehicle users, hindering the adoption and usage of electric vehicles.
Innovation Solution
An electric-vehicle power management system that integrates a processing hardware unit with multiple modules to predict state-of-charge, determine optimal charging routes, and identify opportunities for vehicle-to-grid energy exchange, using a user's itinerary and geographic location of charging stations, while considering factors like charging station capabilities and user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If remote charging stations are increased in number, then user range anxiety is reduced, but infrastructure investment cost increases
Solution Approach 1:
The system performs preliminary routing planning that proactively identifies charging stations along the user's itinerary before the vehicle reaches low charge states. By predicting future charging needs based on the itinerary and current charge level, the system prepares optimal routes in advance, allowing users to reach appointment locations without experiencing range anxiety during travel.
Solution Approach 2:
The vehicle's onboard system autonomously manages its own charging needs by integrating with the user's calendar itinerary. The system automatically calculates energy consumption based on route distance, identifies suitable charging stations along the path, and determines optimal charging timing without requiring external intervention or manual planning by the user.
2Productivity
If the system integrates detailed itinerary analysis and multiple routing options, then charging optimization improves, but system complexity increases
Solution Approach 1:
The system integrates multiple functions into a unified power management architecture: itinerary parsing, energy consumption calculation, routing optimization, and charging station identification all operate within a single integrated system. This multi-functional approach allows the system to handle diverse charging scenarios (different vehicle types, charging station capabilities, user preferences) through a common framework rather than separate specialized modules.
Solution Approach 2:
The system dynamically adjusts routing parameters based on real-time conditions and user preferences. It evaluates multiple routing options by changing parameters such as charging stop duration, route distance, and charging station selection criteria. The system can modify these parameters to balance different objectives (fastest route vs. most charging opportunities vs. lowest cost) without requiring fundamentally different system architectures.
Data Source
AI summary
An electric-vehicle power management system including a processing hardware unit and multiple modules executable by the processing hardware unit. The modules include a calendar module configured to, by way of the processing hardware unit, obtain a user itinerary indicating multiple appointment locations to be visited by a user of an electric vehicle and associated times to visit the appointment locations. The modules also include a routing module configured to, by way of the processing hardware unit, determine optional routes connecting the appointment locations indicated by the itinerary, and a vehicle-energy module configured to, by way of the processing hardware unit, predict states of charge, or changes in state of charge, for the vehicle in connection with the optional routes, yielding state-of-charge predictions. The routing module determines selected routing based on the state-of-charge predictions from the vehicle-energy module. Analogous methods and computer-readable storage devices are also provided.