EV Charging Plan Using Weather Forecasts and Renewable Preference
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Solution Overview
Problem
Existing charging systems do not effectively consider a user's orientation towards renewable energy sources when planning charging sessions, leading to inefficient and potentially costly charging decisions.
Innovation Solution
A charging management system that utilizes correlation information linking charging stations, weather data, and user preferences to derive a charging plan that minimizes costs while aligning with the user's preference for renewable energy sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If charging decisions are made without considering user orientation towards renewable energy, then charging cost optimization is simplified, but user environmental preferences are not reflected
Solution Approach 1:
The system performs preliminary actions by acquiring user orientation information in advance and storing correlation information between charging stations, weather conditions, and charging unit prices before actual charging decisions are made. This allows the system to pre-process multiple factors and present optimized charging plans that simultaneously consider cost and user preferences without complicating the user interface.
2Productivity
If charging plans consider multiple factors including weather forecasts and renewable energy sources, then charging cost reduction and user preference alignment are improved, but system complexity increases
Solution Approach 1:
The system segments the complex charging optimization problem into distinct functional modules: a weather forecast information acquirer that handles meteorological data, an SOC decrease amount estimator that predicts battery state changes, a user orientation information acquirer that captures user preferences, and a charging plan deriver that synthesizes all inputs. Each module processes specific aspects independently, reducing overall system complexity while maintaining comprehensive optimization.
Solution Approach 2:
The system introduces correlation information as an intermediary data structure that pre-establishes relationships between charging stations, weather conditions, and charging prices. This intermediary layer simplifies the processing burden on the charging plan deriver by providing pre-processed, contextualized data rather than requiring real-time analysis of all raw inputs simultaneously.
3Loss of time
If charging plans are derived considering future SOC decrease amounts and weather forecasts, then charging timing optimization is improved, but information processing requirements increase
Solution Approach 1:
The system performs preliminary estimation of future SOC decrease amounts based on historical data and predicted driving patterns before final charging decisions are made. This advance estimation allows the charging plan deriver to focus processing resources on optimizing charging timing and station selection rather than calculating SOC projections in real-time, reducing information processing burden while maintaining timing optimization accuracy.
Data Source
AI summary
A charging management system comprising one or more processors configured to acquire weather forecast information indicating weather forecasts. The one or more processors are configured to estimate amounts of future decrease in a state of charge of an onboard battery in any vehicle. The one or more processors are configured to acquire user's orientation information indicating a user's orientation as to whether a user is oriented toward charging at a charging station using renewable energy as a source of power generation. The one or more processors are configured to derive, based on correlation information stored in a storage device, the acquired weather forecast information, the estimated amounts of decrease in the state of charge, and the user's orientation information, a charging plan indicating a combination of a charging timing and a charging place in a future such that a charging unit price is relatively reduced while reflecting the user's orientations.