With artificial intelligence towards better battery understanding
The Munich-based startup TWAICE cooperates with the Institute of Automotive Technology at the Technical University of Munich in the research project bawaii to improve predictive battery analytics with the application of artificial intelligence.
Already at 70 to 80 percent of its capacity, the battery in electric cars needs to be replaced to ensure reliable operation. However, the condition and the remaining lifespan can to date not yet be determined precisely, often making the operation of electric cars inefficient and expensive. The Munich-based startup TWAICE has developed software that will change that. With the help of digital twins, the program provides precise information about the “health status” of a lithium-ion battery. The industry is very interested in this: car manufacturers want to use the software as well as energy companies and insurance companies. In advancing their innovative approach, the founders of TWAICE are now working closely together with the Technical University of Munich (TUM). This cooperation between research and industry is under the project name bawaii (Battery Analytics with Artificial Intelligence). It opens a way for the latest academic research on artificial intelligence to be put to practical use.
Researchers at the TUM Institute of Automotive Technology (FTM) have been using artificial intelligence for a while to make e-vehicles cheaper, give them more range and bring new models to the market quicker. The research group works on batteries, the most valuable and important part of an electric car. TWAICE continues to use the same approach for practical applications outside university. The startup was launched a year ago as a spin-off from TUM. Meanwhile, some industrial corporations are already among the customers. They want to use the TWAICE software to run their batteries more efficiently.
TWAICE was founded by Michael Baumann and Stephan Rohr. The two engineers have long been concerned with the question of how to determine the health of modern batteries reliably. They use sophisticated software to observe the processes in expensive energy storage systems. From this, the developers can derive predictions on the condition and life of the battery. These factors depend on the individual stress that must be recorded during operation by constant measurement data.
The government-sponsored project bawaii is intended to provide further opportunities for meaningful analyses of the large amounts of data generated when operating a battery. The TUM researchers at the institute of Professor Markus Lienkamp have a far-reaching competence with regard to the behavior of lithium-ion batteries. They use special modeling and simulations for their investigations. The institute has numerous test benches and demonstrators where researchers can verify their calculations. Part of the task sharing is that TUM explores new methods and validates existing ones, while their partners from TWAICE deliver the underlying AI software and take on the testing of the newly developed method in real vehicles.
Vehicle manufacturers can use TWAICE software to precisely validate how well their battery system meets their needs. And they receive accurate predictions for the aging of the battery right from the development stage. This can then be designed accordingly. The potential savings can be huge because so far, the energy storages are often planned too big. Up to half of the cost of an e-car is in the battery. How profitable such a vehicle is at the end depends crucially on how well its battery can be utilized. The bawaii project will increase the benefit through the targeted use of artificial intelligence.
TWAICE supports enterprises across industries with predictive battery analytics software based on digital twins. Customers are empowered to develop and use battery systems more efficiently and sustainably while making them more reliable and durable. Precise predictions of battery conditions and ageing significantly optimize battery development and use. Exact determination of current condition also enables certification of batteries for reuse and 2nd life. TWAICE was founded in 2018 as a spin-off from Technical University of Munich after four years of research and is headquartered in Munich. Clients come from industries ranging from e-scooters over automotive to stationary energy storage.
Press Contact: Lennart Hinrichs | Commercial Director | Tel.: +49 89 997 324 58 | E-Mail: email@example.com
About Technical University of Munich (TUM)
With about 550 professors, 42,000 students and 10,000 employees, the Technical University of Munich (TUM) is one of the strongest research universities in Europe. The areas of specialization are engineering, natural sciences, life sciences, and medicine, combined with economics and social sciences. The TUM acts as an entrepreneurial university that promotes talent and creates added value for society. It benefits from strong partners in science and business. It is represented worldwide with the TUM Asia campus in Singapore as well as liaison offices in Brussels, Cairo, Mumbai, Beijing, San Francisco, and São Paulo. Nobel laureates and inventors such as Rudolf Diesel, Carl von Linde, and Rudolf Mössbauer have been researching at the TUM. The University of excellence status was awarded in 2006 and 2012. In international rankings, the TUM regularly belongs to the best universities in Germany.
About the Institute of Automotive Technology (FTM), TU Munich
The main research at the Institute of Automotive Technology under the supervision of Prof. Markus Lienkamp is about the demands in mobility. Particular focus is on electromobility as well as its components and infrastructure. To support that there are five research groups with different areas of studies: Vehicle Dynamics, Driver Assistance and Control Systems, Vehicle Concepts, Electric Vehicle Components, and Smart Mobility.
bawaii is funded by the Bavarian Ministry of Economic Affairs, Land Development and Energy i. R. of the R & D Program Information and Communication Technology Bavaria, IUK-1808-0013.