EV Charging Control Using Cost and Greenness Forecasts
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Solution Overview
Problem
Existing systems for controlling the charging of electric vehicle energy storage devices do not effectively optimize charging based on cost and environmental impact, leading to inefficiencies in energy usage and increased greenhouse gas emissions.
Innovation Solution
A computer-implemented method and system that receives user optimization inputs, energy cost and greenness forecast data, driver behavior data, energy storage device data, and electric vehicle usage data to generate a chargeability quotient. This quotient indicates the suitability of multiple time intervals for optimal charging, allowing the system to control charging accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If charging is performed without optimization based on cost and environmental factors, then charging operation is simple, but energy cost and greenhouse gas emissions increase
Solution Approach 1:
The system performs preliminary actions by forecasting energy cost and greenness data in advance, then uses this predicted information to determine optimal charging time intervals before actual charging occurs. This allows the system to proactively schedule charging during low-cost, high-greenness periods rather than reactively responding to current conditions.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously monitoring actual charging results and comparing them against predicted outcomes. The chargeability quotient calculation integrates multiple data sources including forecasted energy costs, greenness metrics, driver behavior patterns, and vehicle usage data to dynamically adjust charging schedules and improve optimization accuracy over time.
2Use of energy by moving object
If charging is performed without considering time intervals and forecasts, then charging process is simple, but energy cost efficiency decreases
Solution Approach 1:
The system applies dynamics by making the charging control adaptive and flexible rather than static. The chargeability quotient is dynamically calculated based on real-time and forecasted data, allowing the charging schedule to automatically adjust to changing energy costs, greenness levels, driver behavior, and vehicle usage patterns. This dynamic approach enables the system to optimize energy cost efficiency while responding to varying conditions.
Solution Approach 2:
The system changes key parameters including energy cost thresholds, greenness thresholds, and chargeability quotient values based on forecasted data and actual conditions. By adjusting these parameters dynamically, the system can identify optimal charging time intervals that maximize energy cost efficiency while adapting to different scenarios such as peak pricing periods or high renewable energy availability.
3Measurement precision
If charging is performed without considering driver behavior and vehicle usage data, then charging control is simpler, but charging suitability accuracy decreases
Solution Approach 1:
The system achieves multi-functionality by integrating multiple data sources and analysis capabilities into a single charging control framework. The chargeability quotient calculation simultaneously considers forecasted energy costs, greenness metrics, driver behavior patterns, and vehicle usage data, allowing the system to perform comprehensive charging suitability assessment while maintaining a unified control mechanism.
Solution Approach 2:
The chargeability quotient serves as an intermediary metric that synthesizes information from multiple diverse data sources including forecasted energy data, driver behavior data, and vehicle usage data. This intermediary calculation translates complex multi-dimensional data into a single suitability score that guides charging decisions, simplifying the integration of multiple data types while maintaining high measurement precision.
Data Source
AI summary
A system and a computer-implemented method of controlling charging of an energy storage device of an electric vehicle are provided. The method may comprise: receiving a user optimization input indicating selection of an optimization factor for the charging of the energy storage device; receiving at least one of energy cost forecast data and energy greenness forecast data; generating a chargeability quotient based on the user optimization input and at least one of the energy cost forecast data and the energy greenness forecast data, the chargeability quotient indicating suitability of multiple time intervals of a control period for optimum charging of the energy storage device; receiving driver behavior data, energy storage device data and electric vehicle usage data; and controlling charging of the energy storage device during the multiple time intervals based on the chargeability quotient, the driver behavior data, the energy storage device data and the electric vehicle usage data.