Systems and methods for optimizing energy consumption on a road trip
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Solution Overview
Problem
Electric vehicle users face inconvenience and higher energy costs when charging during peak hours, leading to inefficient energy consumption and increased emissions during long trips.
Innovation Solution
A road trip planning system that optimizes energy consumption by recommending train and carshare EV combinations, pre-conditioning personal vehicles, and strategically charging at optimal stations based on real-time data to minimize emissions and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the user charges the EV frequently during peak hours, then the EV battery is kept charged, but the energy cost increases and user convenience deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-conditioning the EV battery before peak pricing hours. It charges the battery during off-peak hours when energy costs are lower, and uses stored energy during peak hours to avoid expensive charging. This anticipatory approach resolves the contradiction by preparing energy storage in advance rather than reacting to peak pricing conditions.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring energy prices, battery charge levels, and trip requirements. It adjusts charging strategies based on real-time price signals and grid conditions, dynamically optimizing when to charge versus when to use stored energy. This feedback loop enables the system to automatically respond to changing conditions and maintain optimal energy management.
2Reliability
If the user charges the EV during peak hours, then the EV is ready for travel, but the energy cost and emissions increase
Solution Approach 1:
The system ensures vehicle readiness through preliminary charging during off-peak hours before the trip begins. By pre-charging the battery when grid emissions are lower, the system guarantees the EV is ready for travel without requiring peak-hour charging. This advance preparation eliminates the need for harmful peak-hour charging while maintaining reliable vehicle availability.
Solution Approach 2:
The system converts the potential harm of peak-hour charging into benefit by using off-peak charging surplus. Energy that could be wasted or cause high emissions during peak hours is instead stored during off-peak hours when renewable energy availability is higher and emissions are lower. The system transforms the charging schedule from a source of harm to a source of environmental benefit.
3Adaptability or versatility
If the user uses personal vehicle for entire trip, then the user has flexibility, but the energy consumption and charging stops increase
Solution Approach 1:
The system segments the trip into multiple transportation modes: personal EV for portions of the journey and public transit for other portions. This segmentation allows the personal vehicle to be used strategically rather than continuously, reducing total energy consumption while maintaining flexibility for the user. The trip is divided into segments that optimize both environmental impact and user needs.
Solution Approach 2:
The system creates a multi-functional travel solution that combines personal vehicle usage with public transit integration. The personal EV serves multiple purposes: local transportation, long-distance travel when necessary, and energy storage asset. This universal approach allows the system to adapt to different trip requirements while reducing overall energy consumption through mode switching.
Data Source
AI summary
A road trip planning system including a transceiver and a processor is disclosed. The transceiver may be configured to receive trip information associated with a user. The trip information may include information associated with a trip source location and a trip destination location. The processor may determine that the user may be traveling via a vehicle between the trip source and destination locations. The processor may further monitor a real-time vehicle geolocation when the vehicle may be traveling between the trip source and destination locations, and predict an estimated time of arrival for the user at the trip destination location based on the real-time vehicle geolocation. The processor may further transmit information associated with the estimated time of arrival to a computing device associated with the trip destination location. The computing device may activate user comfort devices at the trip destination location based on user's estimated time of arrival.