Investigation of an Automotive Battery Pack Cell

Abstract

Lithium-ion batteries are used in various applications, e.g. in electric vehicles. Depending on the application, batteries are either used as single cells or combined in small or big size battery packs. The performance of battery packs is usually controlled by battery management systems (BMS) in order to ensure operation-mode safety and to prolong the battery-pack life by cell balancing. However, profound knowledge about factors influencing the quality and half-life time of batteries enables higher reliability in battery pack performance. In this study an electric vehicle battery pack is examined, which was removed from the car and disassembled. Three parts of the battery pack were analyzed (the BMS, the pack design and the cell itself). In this publication, the detailed results for a cell which showed a considerable lower capacity than all the other cells during electric pack-testing are presented. The cell type as found in the battery pack was investigated in four different conditions: new cell with no prior usage, cell used in an original car battery pack, worst cell of the pack, cell academically aged on a test stand. The new cell and the academically aged cell can be compared to determine the cell status at the beginning and at the end of their lifetime. These time markers allow a classification of the state of health of the original car cell. Comparing the cells requires a solid data basis. Therefore, a complete parameter identification for a (physical-chemical battery) model parameterization compared to the procedure (as described in Ecker et. al. 20151) was carried out. Applying a physical-chemical battery model which is based on fundamental equations of the battery processes2 has two advantages: The model delivers the current/voltage response of a cell as well as detailed information about the intrinsic parameters of a cell. The accuracy of the simulation is significantly influenced by the parameterization that should consider diffusion parameters of the electrolyte and the active material as well as geometric quantities or exchange current densities. These parameters can be determined by electric measurements and a post mortem analysis of the cell. To optimize the parameter results, new techniques are applied. The parameterization can be used later on to predict the behavior of a battery in a specific application, but also onboard for diagnostic issues. One of the parameters which can be determined by various techniques is the porosity. In this case, the identification of the porosity is shown by Hg-Porosimetry and by Nano-CT images. The Nano-CT images of the new and the aged cell can be compared and offer valuable insights in the structural changes during the ageing process. These measurements also deliver results of the tortuosity, the pore size distribution, the particle size and shape. Furthermore the exchange current density is determined by electrochemical impedance spectroscopy with the full cell and laboratory cells. One particular challenge for realizing the described approach is the lack of insights regarding the allocation of the two charge transfer processes identified in the spectra to the anode and the cathode. To overcome this challenge, two steps have been undertaken: First, the cell was opened in a post mortem routine and laboratory cells were built out of the electrode materials together with lithium as counter electrode. Second, electrochemical impedance spectra measurements were executed on the new built laboratory cells. With the parameter set described in this study, differences of the cells in the automotive battery pack can be determined and evaluated. The results reveal that the cell itself did not cause the differing behavior of the low capacity cell compared to the other battery pack cells. Nevertheless, the results can be applied to improve the quality of BMS, which can follow significant parameters and react on critical states with higher precision. 1 M.Ecker, et all. Parametrization of a Physico-Chemical Model of Alithium-Ion Battery doi:10.1149/2.0551509 J. Electrochem. Soc. 2015 volume 162, issue 9, A1836-A1848 2 J. Newman et all., Porous-electrode theory with battery applications, doi: 10.1002/aic.690210103, AIChE Journal, 1975