Community Energy Storage Control With EV Battery Aging Feedback
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Solution Overview
Problem
The increasing integration of renewable energy sources and electric vehicles in power grids creates uncertainty and volatility in energy supply, leading to unstable electricity prices and potential power outages, while existing V2G solutions neglect battery aging, resulting in inefficient energy management and premature battery degradation.
Innovation Solution
An edge computing device optimizes energy storage strategies for households by incorporating a battery aging model, allowing for bidirectional charging and discharging of electric vehicle batteries to balance supply and demand, while minimizing battery degradation and energy costs through smart charging and Vehicle-To-Everything (V2X) infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If V2G solutions are implemented to balance supply and demand, then energy management efficiency is improved, but battery degradation accelerates
Solution Approach 1:
The system dynamically adjusts charging/discharging parameters (rate, duration, timing) based on grid conditions and battery state to optimize the trade-off between energy management efficiency and battery degradation. The edge computing device modifies operational parameters in real-time to minimize harmful effects while maintaining productivity benefits.
Solution Approach 2:
The system implements continuous feedback loops where battery state (charge level, temperature, degradation indicators) and grid conditions are monitored, and charging/discharging decisions are adjusted accordingly. This feedback mechanism allows the system to learn from past operations and optimize future decisions to balance efficiency and battery longevity.
2Productivity
If centralized optimization is used for V2G solutions, then energy allocation is improved, but computation load increases and single point of failure risk arises
Solution Approach 1:
The system divides the optimization problem into hierarchical levels: cloud computing handles high-level strategic optimization and data aggregation, while edge computing devices handle local tactical optimization. This segmentation distributes computation load, reduces central processing requirements, and eliminates single points of failure by enabling autonomous local decision-making.
Solution Approach 2:
The edge computing device acts as an intermediary between the cloud-based centralized system and local V2G operations. It receives optimization goals from the cloud, processes local data, and executes decisions autonomously when connected, or independently when disconnected, thereby reducing computation load on central systems while maintaining allocation efficiency.
3Loss of energy
If battery charging is increased during low price periods, then energy cost is reduced, but battery aging accelerates
Solution Approach 1:
The system dynamically adjusts charging strategies based on real-time battery state and grid conditions rather than following static low-price-period schedules. When battery state indicates vulnerability to degradation (extreme temperatures, high charge levels), the system modifies or pauses charging even during low-price periods, creating a dynamic balance between cost reduction and battery protection.
Solution Approach 2:
The system performs preliminary assessment of battery state before initiating charging during low-price periods. If conditions are favorable (moderate temperature, appropriate charge level), charging proceeds; if conditions indicate risk of accelerated aging, the system takes preliminary protective action by delaying or modifying charging, thereby preventing future degradation while still capturing cost savings when safe.
Data Source
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AI summary
The disclosure relates to a device (200) configured to optimize an energy storage strategy of a community (112) comprising a plurality of households (108). Further, the disclosure relates to a cloud computing device (300) configured to optimize an energy storage strategy of a community (112) comprising a plurality of households (108). Further, the disclosure relates to an electric vehicle (102) associated with a household (108) in a community (112) comprising a plurality of households (108), the electric vehicle comprising (102) a battery aging model indicative of a battery aging status of a battery pack of said electric vehicle (102). Further, the disclosure relates to methods (400, 500) directed to optimize energy storage of households and communities.