Controlling module pressure evolution over lifetime with buffer layers: Design guidelines, prediction and validation for automotive systems

The development of cheap, energy dense lithium-ion battery systems for electric vehicles is pushed with enormous effort. Stacking pouch-type cells in modules achieves the highest energy densities. As a consequence, however, such modules require an outer bracing to provide a medium pressure environment for optimal cell operation in terms of performance, safety and lifetime. Pouch […]

Deep learning-based state-of-health estimation with battery charging data

The scope of this work is the development of a data-driven capacity estimation model for cells under real-world working conditions with recurrent neural networks having long short-term memory capability. Voltage-time sensor data from the constant current phase charging curve is used as input, reflecting input availability in the real world. The network achieves a best-case […]

Flexible, fast and transparent approaches to battery capacity forecasting

Battery degradation is a complex phenomenon, leading many to use data-driven techniques to predict future battery health [1]. Linear models are transparent, easy to implement and not computationally demanding. However, simple linear models are often combined with Kalman filters and particle filters when modelling battery degradation [1]. The need for the filtering techniques suggested that […]

How temperature inhomogeneities affect the cycle life of Li-ion batteries

Looking at the ageing behaviour of Li-ion batteries, the internal cell temperature is one main influencing factor. The quantification of its influence is one of the key aspects to improve cycle life and is increasingly addressed in literature. In general, an optimum temperature of about 25 °C is given with increasing degradation both at rising […]