EV Charging Station Scheduling With Robotic Charger Interchange
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Solution Overview
Problem
The overstay issue in plug-in electric vehicle (PEV) charging stations, where chargers remain occupied for extended periods, reducing availability and hindering access for new vehicles, exacerbates the inadequate charging infrastructure problem, despite efforts like infrastructure upgrades, penalty/incentive designs, and interchange operations.
Innovation Solution
Implementing a mixed-type charger charging station (MCCS) with a combination of fixed and robotic chargers, where robotic chargers proactively plug and unplug vehicles, and using mixed-integer linear programming (MILP) to optimize charger assignments, plug-in/out schedules, and charging power, to maximize throughput and profit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If infrastructure upgrades are implemented to enhance charging capacity, then the charging service capability is improved, but capital investment and grid demand charge costs increase considerably
Solution Approach 1:
The patent implements dynamic charger assignment and interchange operations where chargers are not statically assigned to vehicles but are dynamically reallocated based on real-time charging status and vehicle departure times. This allows the same physical charging infrastructure to serve more vehicles over time, enhancing service capability without proportional capital investment.
Solution Approach 2:
The system changes operational parameters by introducing flexible charging schedules and interchange operations that allow vehicles to be unplugged before their declared departure times. This transforms the static charging process into a dynamic one, improving throughput without requiring additional charging ports or infrastructure.
2Productivity
If interchange operations are implemented to reduce overstay, then charger availability is improved, but device complexity and operational management difficulty increase
Solution Approach 1:
The system implements self-service through automated optimization algorithms that autonomously determine interchange operations, charger assignments, and unplugging schedules. The optimization model automatically processes vehicle arrival/departure information and makes charging decisions without requiring complex manual coordination, reducing operational management complexity while improving charger availability.
3Productivity
If robotic chargers are deployed to perform proactive plug and unplug operations, then service capacity is enhanced, but device complexity and initial investment costs increase
Solution Approach 1:
The patent introduces an optimization management system as an intermediary that coordinates between vehicles, chargers, and operational decisions. This central intelligence layer handles the complexity of robotic charger operations, charger assignments, and interchange scheduling, allowing robotic chargers to enhance service capacity while the management system absorbs the operational complexity.
4Productivity
If charging stations are equipped with more fixed chargers to meet demand, then service capability is improved, but loss of time for new vehicle arrivals increases due to overstay occupation
Solution Approach 1:
The system implements early unplugging operations where chargers are released back to the pool of available resources before vehicles' declared departure times. This allows the same chargers to be recovered and reassigned to waiting vehicles, reducing the effective occupation time and minimizing loss of time for new vehicle arrivals while maintaining service capability.
Data Source
AI summary
A method for operating an electric vehicle charging station that comprises a first number of fixed chargers and a second number of mobile devices. Each of the mobile devices moves in the charging station to plug and unplug an electric vehicle. The method includes, at a time step, obtaining, upon receiving a charging request from an electric vehicle arriving at a beginning of the time step, a first charging demand; deriving, upon receiving charging dynamics of an electric vehicle having been staying at the charging station before the time step, a second charging demand; generating, with respect to an optimization horizon including the time step and a plurality of subsequent time steps, a charging demand forecast; and solving, with respect to the optimization horizon, an optimal operation solution, based on the first charging demand, the second charging demand, and the charging demand forecast.