BattLifeBoost: Life cycle extension of existing and future stationary battery storage systems through hybrid condition prediction

The BattLifeBoost project aims to improve the condition estimation and service life prediction of stationary battery storage systems. Therefore, the project consortium is developing a system model for estimating the condition and service life. A hybrid approach is being pursued in the development of the model. On the one hand, a physico-chemical ageing model will be developed based on extensive data from a laboratory study, and on the other hand, state of health estimates will be obtained from several years of field data from a home storage provider using machine learning algorithms and contribute to the development of the system model.
The EES is conducting research into physico-chemical ageing modeling. To this end, the initial focus is on the development of physico-chemical ageing equations for the cell under consideration in the project. The ageing equations are parameterized and validated on the basis of an extensive data set from a laboratory study preceding the project. Based on this, a method for hybrid parameterization of physico-chemical aging models is developed, which links the previous, laboratory data-driven parameterization with real field data.

The aim is to develop an independent ageing model based on physical-chemical understanding that can correctly simulate the ageing behavior of the cell in any load scenarios that deviate from the parameterization and field data. The developed model is then used to assess the influence of different load scenarios on the remaining service life, e.g. the additional use of home storage systems in grid-supporting applications.

The EES is working on the BattLifeBoost project in cooperation with Sonnen GmbH, the University of Applied Sciences Kempten and the BMW Group.