The CTC Technology Centre is developing a new methodology to assess the performance and lifetime of the cells that make up lithium-ion batteries used in aerospace applications. The primary objective of this research is to reduce the testing times from two years to less than five months. The project is being tendered by the European Space Agency on a competitive basis through the Technology Development Element programme.
Electrochemical cells are the active core of battery systems, the elements that convert chemical energy into electrical current. To assess the durability and efficiency of a battery, accelerated testing and evaluation of the cells is carried out to determine their performance at different voltages, analyse their individual reaction to various load levels and finally, calculate their lifetime. The main problem with these accelerated tests is their duration, which can be up to two years.
The only technology centre in Cantabria is developing an innovative methodology that will reduce the time spent on these tests to less than five months. CTC seeks to combine existing automotive battery knowledge with Artificial Intelligence and advanced simulation techniques to accelerate cell characterisation.
Different electrochemical tests will be conducted on the cells to allow a qualitative assessment of their performance and predictive analysis of the fatigue or damage level. Once this data has been collected, it will be processed by custom software developed by CTC to generate a virtual representation of the cells and battery. In this way, new tests can be performed on multiple time references and the data already collected manually on the behaviour of cells can be extended.
Conducting tests in a virtual representation has several benefits. The first and most relevant to the research is time optimisation. The software allows more tests to be performed in a much shorter period. It also means reducing the materials used for testing, thus reducing research costs.
In addition, CTC is looking to add Deep Learning technology to the software to optimise battery efficiency. This additional functionality will use test data to learn from and anticipate possible errors.
In this sense, this research is linked to three of the technology centre’s main areas of specialisation. Artificial Intelligence and Deep Learning, advanced modelling and simulation and materials characterisation are the technological lines of work that come together in this initiative and on which CTC develops an important part of its activity.
