Electric Vehicle Charging Management System for Cost Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing electric vehicle charging systems do not dynamically adjust charging times based on electric charges, leading to increased power consumption during peak hours, inefficiency in energy use, and higher costs for users.
Innovation Solution
A system and method that utilize a message processing unit, charging time/charging cost calculator, and determining unit to dynamically set charging times based on electric charges within a desired charging time zone, allowing for optimization of charging costs by recalculating and redistributing costs among users with movable charging tolerance times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If charging is performed immediately upon user request, then user convenience is improved, but power consumption during peak hours increases and charging cost rises
Solution Approach 1:
The system dynamically adjusts charging schedules based on real-time power network conditions, user preferences, and predicted usage patterns. The charging time is not fixed but adapts to changing conditions, allowing the system to shift charging loads away from peak periods while still meeting user needs.
Solution Approach 2:
The system performs preliminary calculations of optimal charging times by analyzing historical data, predicted power consumption patterns, and user preferences before actual charging occurs. This advance planning enables the system to schedule charging during off-peak hours while ensuring vehicles are ready when needed.
2Ease of operation
If charging is concentrated in evening hours, then user convenience is improved, but maximum power consumption amount increases and additional generator installation is required
Solution Approach 1:
The system distributes charging loads across different time periods rather than concentrating them in evening hours. By implementing periodic charging schedules that utilize off-peak, mid-peak, and peak periods strategically, the system smooths out power demand and prevents excessive maximum power consumption.
Solution Approach 2:
The system dynamically adjusts charging schedules based on real-time power network conditions, user preferences, and predicted usage patterns. The charging time is not fixed but adapts to changing conditions, allowing the system to shift charging loads away from peak periods while still meeting user needs.
3Reliability
If charging time is fixed according to user request, then charging reliability is improved, but charging cost increases during high electric charge periods
Solution Approach 1:
The system performs preliminary calculations of optimal charging times by analyzing historical data, predicted power consumption patterns, and user preferences before actual charging occurs. This advance planning enables the system to schedule charging during off-peak hours while ensuring vehicles are ready when needed.
Solution Approach 2:
The system changes the timing parameter of charging operations based on electric charge rates, power consumption patterns, and user preferences. By adjusting the time parameter dynamically while maintaining the charging amount and vehicle readiness requirements, the system reduces costs without sacrificing reliability.
4Loss of energy
If dynamic charging time setting is implemented, then charging cost is optimized, but device complexity increases
Solution Approach 1:
The system automatically determines optimal charging times by analyzing power consumption data, electric charge rates, and user preferences without requiring complex user input or manual scheduling. The charging management apparatus self-adjusts parameters based on received information, reducing the need for complex external control mechanisms.
Solution Approach 2:
The system uses feedback from power consumption measurements, electric charge rate information, and user preferences to continuously optimize charging schedules. By implementing closed-loop control where charging decisions are adjusted based on actual outcomes and changing conditions, the system achieves cost optimization through relatively simple iterative adjustments rather than complex predetermined algorithms.
Data Source
AI summary
Disclosed are a system and a method for managing battery charge of an electric vehicle according to the present invention. A system for managing battery charge of an electric vehicle according to the present invention may include: a message processing unit to receive, from a management center, power information about a smart grid that supplies power, to receive, from a first user, user information for setting a charging condition, and to provide set charging information; a charging time/charging cost calculator to calculate a first charging tolerance time and a first charging cost of the first user who has requested charge according to a charging mode included in the user information; and a determining unit to select a charging time zone of a minimum cost within a tolerance time based on content calculated by the charging time/charging cost calculator, and to generate charging information.


