EV Charging Server Control for SoC-Aware V2G Profit Scheduling
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Solution Overview
Problem
Existing vehicle-to-grid (V2G) systems lack an efficient method to optimize charging and discharging operations based on electricity rates and user behavior to maximize profit and user convenience.
Innovation Solution
A server and control method that determines a desired state of charge (SoC) of a vehicle, controls charging and discharging to align with electricity rate fluctuations, and adjusts electricity rates to prioritize charging at lower rates and discharging at higher rates within the same time period, maintaining a high SoC level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If charging and discharging operations are performed repeatedly in existing V2G systems, then revenue generation is enabled, but system efficiency and profit maximization are insufficient due to lack of optimization based on electricity rates and user behavior
Solution Approach 1:
The system dynamically adjusts charging and discharging parameters based on electricity rate fluctuations, user behavior patterns, and vehicle state of charge. The server modifies charging power levels, timing, and duration to optimize profit while maintaining system efficiency, transforming static charging operations into adaptive dynamic operations.
Solution Approach 2:
The server continuously monitors electricity rate information, user behavior data, vehicle SoC levels, and charging/discharging performance. This feedback loop enables the system to learn from past operations and adjust future charging schedules to maximize revenue while maintaining optimal system efficiency and user satisfaction.
2Ease of operation
If charging is performed at higher electricity rates to meet user demand quickly, then user convenience is improved, but operating profit decreases
Solution Approach 1:
The server performs preliminary analysis of electricity rate patterns, user behavior preferences, and vehicle requirements before executing charging operations. By predicting optimal charging windows and preparing advance charging schedules, the system secures user convenience through guaranteed service while capturing lower electricity rates to maximize operating profit.
Solution Approach 2:
The system dynamically adjusts charging strategies based on real-time electricity rate fluctuations and user demand patterns. Rather than using fixed charging schedules, the server adapts charging power levels and timing dynamically, allowing users to receive charging service when needed while the system exploits rate variations to improve profitability.
3Productivity
If the V2G system operates without optimized control methods, then system simplicity is maintained, but profit maximization and user satisfaction cannot be achieved
Solution Approach 1:
The server acts as an intermediary between the charger, vehicle, and power grid, centralizing the complexity of optimization algorithms, electricity rate analysis, and user behavior modeling. This intermediary approach enables profit maximization through sophisticated control while keeping the actual charging hardware and user interfaces relatively simple and manageable.
Data Source
AI summary
A server may include a transceiver configured to communicate with a charger, and a controller configured to determine, based on state information of a vehicle, a required state of charge (SoC), control, based on charging and discharging from an SoC of the vehicle to the required SoC and electricity charging costs over periods, the charger to increase (e.g., maximize) an expected profit, and while controlling the charger, adjust the electricity charging costs over the periods so that a first electricity charging cost at a start time of a first period of the periods is lower than a constant electricity charging cost over the first period, and the first electricity charging cost is gradually (e.g., linearly) increased over the first period.