Simulation and real-life assessment of cell-to-cell variation of ageing lithium-ion batteries

Dechent, Philipp André; Sauer, Dirk Uwe (Thesis advisor); Howey, David (Thesis advisor)

Aachen : RWTH Aachen University (2021, 2022)
Book, Dissertation / PhD Thesis

In: Aachener Beiträge des ISEA 163
Page(s)/Article-Nr.: 1 Online-Ressource : Illustrationen, Diagramme

Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2021


One of the essential components of an electric vehicle is its energy storage system. Unfortunately, it has also proven to be the most expensive component, limiting the vehicle's performance, for example, range or power, for a given cost target. If higher spreads and more inferior cell quality can be coped within the system, costs can be lowered by decreasing the number of cells rejected in production. In addition, it is essential, particularly for large storage systems such as automotive or stationary storage applications, to ensure the extended usability of the systems. Especially in stationary applications, design lifetimes have to be in the range of 10 to 15 years. Therefore, the manufacturers need to ensure long-lasting battery modules as the smallest exchangeable units. Previous analysis on the impact of variations in commercial lithium-ion battery systems on ageing showed the vital role of spreads in cell parameters of the batteries. The research in this thesis aims to optimise system topologies for individual applications to find suitable cells, avoid oversizing battery systems and give forecasts of a lifetime and quantifiably failure rates for battery packs while decreasing cost. A simulation toolchain was developed to incorporate variability and spread of ageing rates in the system design process. In the scope of the simulation tool, battery topologies can be simulated with varying usage profiles, cell parameter spreads and varying ageing rates. Overall, with the deeper understanding and quantification of cell-to-cell variation that has been developed within this work, advances in several aspects have been achieved. Most relevant is target-oriented testing of batteries incorporating variation for enhanced statistical certainty, resulting in decreased cost and testing efforts through a predetermined sample size. In addition, lifetime prognosis models with additional confidence intervals allow representative predictions of lifetime and failure scenarios in the application.