EV Energy Storage Discharge Estimation for Home Backup Power
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Solution Overview
Problem
Existing electric vehicles face challenges in accurately estimating and managing the discharge time of their energy storage systems when providing power to a home, especially during power outages or shortages.
Innovation Solution
A system and method that utilizes sensors and processors to obtain vehicle and home data, including parasitic loads, geographic location, time of year, and appliance usage, to calculate discharge time, and adjust based on user inputs and machine learning algorithms to manage energy distribution efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing techniques are used to estimate discharge time, then the system is simple, but the estimation accuracy is insufficient
Solution Approach 1:
The system segments the discharge time estimation into multiple components: obtaining sensor data from the vehicle, obtaining home data including multiple characteristics, and calculating discharge time based on both datasets. This segmentation allows each component to be optimized independently while improving overall estimation accuracy.
Solution Approach 2:
The system performs preliminary actions by obtaining sensor data and home data before calculating the discharge time. This includes collecting information about vehicle characteristics, home characteristics, and power requirements in advance, which enables more accurate estimation without adding complexity to the core calculation.
2Productivity
If multiple home characteristics are considered in calculation, then the energy distribution optimization is improved, but the calculation complexity increases
Solution Approach 1:
The calculation system is designed to handle multiple types of inputs universally: sensor data from the vehicle, home characteristics (physical size, geographic location, time of year, time of day), appliance listings, and power consumption curves. This multi-functional approach allows the same system to optimize energy distribution across diverse scenarios without requiring separate calculation mechanisms.
Solution Approach 2:
The system changes parameters dynamically by adjusting the discharge time calculation based on varying inputs such as home characteristics, appliance usage patterns, and power consumption curves. This allows the system to adapt to different energy distribution scenarios while maintaining a unified calculation framework.
3Measurement precision
If machine learning algorithms are used for updated discharge time calculation, then the estimation accuracy improves over time, but the processing requirements increase
Solution Approach 1:
The system performs preliminary calculations using traditional methods to establish baseline discharge time estimates. Machine learning algorithms then refine these estimates by comparing actual power consumption with predicted consumption. This preliminary action reduces the processing burden on the machine learning component while still achieving high accuracy.
Solution Approach 2:
The machine learning algorithm receives feedback from actual power consumption data and updates its predictions accordingly. This feedback mechanism allows the system to improve estimation accuracy over time without requiring excessive processing power, as the learning occurs incrementally based on real-world observations.
4Loss of energy
If appliances are automatically controlled based on power consumption curves, then energy efficiency is improved, but the system automation complexity increases
Solution Approach 1:
The system applies different control strategies to different appliances based on their individual characteristics and power consumption patterns. Rather than uniformly controlling all appliances, the system selectively manages specific appliances based on local conditions such as current power consumption, home characteristics, and discharge time estimates. This localized approach improves energy efficiency without requiring complex centralized control of all devices.
Data Source
AI summary
A method includes obtaining sensor data for a vehicle having an energy storage system that is configured provide power for a home under certain conditions; obtaining home data as to a plurality of characteristics that pertain to power requirements for the home; and calculating, via a processor, a discharge time for the energy storage system based on the sensor data for the vehicle and the plurality of characteristics of the home.