Allgemeine Informationen rund um die Kurse von C. Rufino
- Dozent/in: Carlos Rufino
Battery modules are composed of strings of cells that can be connected in series, parallel, or hybrid. Measuring or estimating individual cell parameters is not trivial and must be performed considering measurement uncertainties, error propagation, and sensor accuracy.
The main functionality of the battery management system is to control the battery charging and discharging process, mainly considering the fast-charging scenarios. To achieve this goal, the BMS measures parameters such as capacity, impedance, and cell temperature and compares them with the desired value after each charging cycle. However, cells age over the years, so the range between individual cell parameters increases, making this control more challenging. Despite recent advances in the literature, studies are still needed to investigate techniques to measure, control, and optimize fast battery charging, considering cell variability.
Cell variability, if not properly controlled, can be responsible for reducing battery life and causing severe battery safety failures. The battery management system must be able to identify and control battery parameters within the response time established by technical safety standards and, above all, before an accident occurs.
The project idea is to develop an initial version of an energy management system. This energy management system will measure and control current, voltage, and temperature signals from the cells, and a dashboard can be developed to monitor the cells in real-time or at a previously configured sampling interval.
- Dozent/in: Carlos Rufino
The objective is to develop a platform that will have the main objective of allowing communication between different devices. For this, an IOT platform will be developed to send data from one computer to another. This system will simulate communication between several ECUs that exist in a vehicle. Communication between the BMS and the communication system will be done using the LoRa protocol. A communication using CAN will be implemented to send and receive data from the vehicle charging station. And finally, wireless or 4G communication will be implemented to send data to the cloud.
- Dozent/in: Carlos Rufino
The proposed student project aims to collect
reliable data and battery aging models capable of making predictions about the
battery condition. In practice, data from different phases of the battery
lifetime will be collected, and each of these databases will enhance the
battery prediction model. Accurately predicting lifespan using early-cycle data
would result in lower costs to manufacture, use and optimize batteries and lead
to wider EV adoption.
- Dozent/in: Carlos Rufino
Electrochemical Impedance Spectroscopy (EIS) is an electrochemical characterization technique that directly measures the impedance characteristics of batteries and estimates the internal state of the battery from these impedance characteristics. There is a growing need for standardized onboard diagnostics (OBD) for electric vehicles to provide accurate health metrics and guarantees for consumers and manufacturers. Conventional EIS measurements are time-consuming. Therefore, building a pipeline to determine the most important EIS frequency measurements for SoH estimation is a critical task in developing a suitable EIS-based OBD.
The selection of appropriate frequencies is also important for fast and efficient onboard diagnostics. The objective of this project is to develop a circuit to generate a square wave signal to excite the battery and measure the battery's nonlinear response. The system will convert an electric vehicle charging station into a tester.- Dozent/in: Carlos Rufino