V2G Optimization for Plug-in Hybrid Electric Vehicles
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Solution Overview
Problem
Current Vehicle-to-Grid (V2G) systems lack optimization to maximize benefits for drivers, utility companies, and society, considering factors like battery state, fuel costs, grid needs, driving habits, and carbon footprint, and are not cost-effective for vehicle participation.
Innovation Solution
A system that includes a plug-in hybrid electric vehicle with a computer that receives data on expected grid conditions and driver needs, controlling battery charging and discharging to optimize state of charge based on grid requirements, using both external power and onboard fuel, and allowing energy exchange with the grid.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If PHEVs recharge from onboard consumable fuel powered means, then the vehicle can operate independently without external electric power sources, but the cost of recharging becomes directly proportional to consumable fuel cost
Solution Approach 1:
The PHEV battery system serves multiple functions: it can be recharged from external electric power sources when available and affordable, or from onboard consumable fuel powered means when external power is unavailable or expensive. This multi-functional capability allows the vehicle to adapt to different operating conditions and minimize recharging costs by selecting the most economical source.
Solution Approach 2:
The system dynamically changes the recharging source parameter based on external conditions (availability and cost of electric power vs. consumable fuel). The control system monitors fuel costs, electric power availability, and battery state of charge to determine the optimal recharging strategy, switching between electric and fuel-based recharging to minimize overall costs.
2Productivity
If PHEVs participate in V2G systems without optimization, then the grid can utilize vehicle battery storage capacity, but the system is not cost-effective for vehicle participation
Solution Approach 1:
The optimization system continuously monitors multiple parameters including battery state of charge, fuel costs, electric power prices, grid needs, and driving habits. This feedback information is used to dynamically adjust V2G participation strategies, ensuring that vehicles only discharge to the grid when it is economically beneficial while maintaining sufficient charge for upcoming trips.
Solution Approach 2:
The system uses predicted future information (expected fuel costs, electric power prices, and driving patterns) to make advance decisions about charging and discharging strategies. This allows vehicles to prepare optimal state of charge levels before V2G events, maximizing economic benefits while ensuring readiness for required trips.
3Productivity
If PHEVs dynamically adjust battery state for grid demands, then grid management is enhanced and economic benefits are optimized, but the system complexity increases
Solution Approach 1:
The optimization system is implemented within the vehicle itself, using onboard computers and sensors to autonomously monitor conditions and control battery charging/discharging. This self-service approach eliminates the need for complex external control infrastructure, as each vehicle independently makes optimization decisions based on local and predicted information.
Solution Approach 2:
The system dynamically adjusts battery state of charge based on real-time and predicted conditions rather than following fixed schedules. The control strategy adapts to changing fuel prices, electric power rates, grid needs, and driving patterns, providing flexible grid management support without requiring rigid complex infrastructure.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Optimizes energy consumption and economic benefits for drivers and utilities, reduces environmental pollution, and enhances grid management by dynamically adjusting battery state to meet grid demands, offering monetary compensation and carbon footprint reduction.
Implementation Method 1
a battery; an electric motor powered by the battery, the battery being rechargeable both from an external electric power source and from a recharging system onboard the vehicle
Implementation Method 2
a consumable fuel powered means, which may be an internal combustion engine (powered by gasoline, diesel, ethanol, natural gas, hydrogen or another combustible fuel) or which may be a hydrogen fuel cell
Data Source
AI summary
In one aspect of the present invention, a vehicle comprises: a consumable fuel powered engine, a battery and an electric motor powered by the battery. The battery is rechargeable both from an external electric power source (such as an electric power grid) and from the consumable fuel powered engine. A computer receives data as inputs and providing outputs, wherein the input data includes an expected state of the electric power source at a time when the vehicle is expected to be coupled to the electric power source. The outputs include control signals to control the state of charge of the battery during the time the vehicle is expected to be coupled to the electric power source.