To design its machine-learning model, this team will apply perturbation signals – a sequence of current signals generated by a power electronic converter – to Li-Ion battery cells. The sequence of signals causes the battery cells to emit electrical responses that can be tested. The team will test the batteries in its lab, and also use power electronic converters to obtain data from batteries installed in electric vehicles. The data, which are generated every minute, measure battery functions such as temperature, voltage and volatility in the currents, resulting in hundreds of thousands of data points. The team is therefore designing an algorithm to assess the data and to design an optimization model.
“An analogy to what we are doing is what was done with chess,” says Mathias Preindl, Professor of Electrical Engineering. “Chess robots work by way of algorithms that study all the moves in all games, and based on that totality, they know all possible moves and can interpret data and select the best moves. That’s what we are trying to achieve with our model.”
“Once we have that, we’ll know when the batteries need to be charged, how long they’ll last, and when they need to be replaced as well as how to extend the life of the battery," he adds. "And since electric cars and Li-Ion batteries are the future, our project has the promise to improve a key part of our transportation system while also improving our environment.”
Learn more about Preindl's lab and research here.