University of Twente Student Theses


Model predictive control for electric vehicle charging using low power microcontrollers

Böhmer, Kevin (2021) Model predictive control for electric vehicle charging using low power microcontrollers.

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Abstract:This research investigates the implementation and performance of model predictive control algorithms for electric vehicle charging on low power microcontrollers with limited resources. The ESP32, ESP32-S2, and ESP8266 boards are chosen as the test platforms, because the boards are cheap and have integrated onboard Wi-Fi. The chosen microcontrollers can be considered as an edge computing node that can provide resilience to the future (smart) power grid. This research investigates which adjustments are needed to run a model predictive scheduling algorithm on a microcontroller with sufficient performance. The performance will be compared with a similar algorithm that runs on a reference system. Such a smart electric vehicle control node can interact with other devices using Demand Side Management. Through performance tests, we demonstrate that the ESP32 is able to run the discrete scheduling algorithm with 1440 intervals in 761.7 milliseconds. The ESP32 needs 7.93 milliseconds to compute a schedule with the continuous scheduling algorithm for the same number of intervals.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Computer Science BSc (56964)
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