Master-slave control scheme in electric vehicle smart charging infrastructure

Area of Science:

• Electrical Engineering
• Computer Science
• Sustainable Energy Systems

Background:

• Growing adoption of plug-in electric vehicles (PEVs) presents challenges for electrical grids.
• Existing charging management systems may not efficiently handle large numbers of PEVs.
• Need for intelligent systems to optimize charging and prevent grid overload.

Purpose of the Study:

• To propose, design, and implement upgrades for the WINSmartEV system.
• To enhance the speed, robustness, and scalability of Level 1 and Level 2 chargers.
• To develop optimal charge scheduling algorithms and an improved vehicle monitoring/identification module (VMM).

Main Methods:

• Integration of new hardware for faster and more scalable Level 1 and Level 2 EV chargers.
• Development of advanced algorithms for optimized Level 2 (EVSE) charge scheduling.
• Implementation of an enhanced VMM for automatic PEV identification and charging authorization.

Main Results:

• Upgraded hardware demonstrates increased charger speed, robustness, and scalability.
• New algorithms achieve more optimal charge scheduling, reducing grid impact.
• Enhanced VMM successfully automates PEV identification and authorization.

Conclusions:

• The upgraded WINSmartEV system effectively addresses challenges in PEV charging management.
• The enhancements lead to improved charging efficiency, grid stability, and user experience.
• WINSmartEV offers a scalable solution for managing a growing number of PEVs.