Model predictive control for electric vehicle charging using low power microcontrollers Kevin Böhmer University of Twente P.O. Box 217, 7500AE Enschede The Netherlands
[email protected] ABSTRACT All the above-mentioned factors lead to severe peaks in This research investigates the implementation and per- the consumption and production in the electricity network. formance of model predictive control algorithms for elec- Demand and supply should be in balance because cheap tric vehicle charging on low power microcontrollers with storage does not yet exist in an electricity network. De- limited resources. The ESP32, ESP32-S2, and ESP8266 mand Side Management (DSM) approaches can flatten out boards are chosen as the test platforms, because the boards these peaks [16]. Many of these DSM algorithms use en- are cheap and have integrated onboard Wi-Fi. The cho- ergy prices as steering signals. Gerards et al. [5] presented sen microcontrollers can be considered as an edge comput- the profile steering algorithm. This algorithm uses desired ing node that can provide resilience to the future (smart) power profiles as steering signals instead of energy prices. power grid. This research investigates which adjustments Van der Klauw [16] provided a scheduling algorithm that are needed to run a model predictive scheduling algorithm can optimize device operations according to these desired on a microcontroller with sufficient performance. The per- power profiles. This paper also shows that steering signals formance will be compared with a similar algorithm that based on energy prices may result in power quality prob- runs on a reference system. Such a smart electric vehi- lems and high losses in performance and that steering us- cle control node can interact with other devices using De- ing profiles yields better results.