SOFTWARE & DATA
Predicting the Impact of Formation Protocols Immediately After Manufacturing
Weng, A., Mohtat, P., Attia, P., Less, G., Lee, S., Stefanopoulou, A.
Description: Forty prismatic lithium-ion pouch cells were built at the University of Michigan Battery Laboratory. The cells have a nominal capacity of 2.36Ah and comprise a NCM111 cathode and graphite anode. Cells were formed using two different formation protocols: "fast formation" and "baseline formation". After formation, cells were put under cycle life testing at room temperature and 45degC. Cells were cycled until the discharge capacities dropped below 50% of the initial capacities. Data was collected by the cycler equipment (Maccor) during both the formation process as well as during the cycling test. Data was processed in the Voltaiq software and subsequently exported as .csv files.
Data: https://doi.org/10.7302/pa3f-4w30
Code: https://github.com/wengandrew/fast-formation
Paper: https://doi.org/10.1016/j.joule.2021.09.015
UofM Pouch Cell Voltage and Expansion Cyclic Aging Dataset
Mohtat, P., Siegel, J. B., Stefanopoulou, A. G., Lee, S.
Description: The focus of this research effort is to systematically study the capability of aging diagnostics using cell expansion under variety of aging conditions and states. The data collection campaign is very important to cover various degradation modes to extract the degradation features that will be used to inform, parameterize, and validate the models developed earlier. In the data collection campaign, we are documenting the evolution of the electrical and mechanical characteristics and especially the reversible mechanical measurement. It is important to note that we collect data using newly developed fixtures that enables the simultaneous measurement of mechanical and electrical response under pseudo-constant pressure.
UofM pouch cell voltage and expansion dataset and modeling code
Mohtat, P., Siegel, J. B., Stefanopoulou, A. G.
Description: The goal here is to study the voltage and expansion response of lithium-ion batteries at different charging rates. Specifically, the goal is to capture the observation of the smoothing of the peaks in dV/dQ and retention of the peaks in d^2 \delta/dQ^2 at higher C-rates. The retention of the peaks at higher charging rates enables better estimation of the cell capacity. To achieve this goal a reduced order electrochemical and mechanical model with multiple particles with a size distribution is developed. This allows us to capture the smoothing and preservation of the phase transitions in the voltage and expansion measurements at high C-rates, respectively. The model is written in Matlab software.
Data: https://deepblue.lib.umich.edu/data/concern/data_sets/kp78gg60j
Paper: https://doi.org/10.1149/1945-7111/aba5d1
An Electro-thermal Model for the BB-2590/U Rechargeable Lithium-Ion Battery
Kim, Youngki; Mohan, Shankar; Siegel, Jason; Stefanopoulou, Anna 2015-12-04
Abstract: This package includes an experimentally validated electro-thermal model of the BB-2590 battery pack (consisting of 24 ICR18650J cells in 3P8S arrangement). The electrical dynamics of the pack is modeled as a three-state equivalent circuit model whose parameters are characterized as functions of SOC (state of charge) and temperature. The thermal dynamics of the pack is derived by assuming that the pack is a brick with a representative a one-state thermal dynamics. The coupled electrical and thermal models have been experimentally validated using the current profile derived from a baseline field test drive-cycle of an iRobot FasTac 510 Robot.
An Electro-thermal Model for the A123 26650 LiFePO4 Battery
Lin, Xinfan; Perez, Hector; Siegel, Jason; Stefanopoulou, Anna
Abstract: An electro-thermal model consisting of an equivalent-circuit electrical model and a two-state lumped thermal model is constructed for an A123 26650 LiFePO4 battery. The electrical and the thermal sub-models are coupled through heat generation and temperature dependency of the electrical parameters. The 5-state model captures the state of charge, voltage, surface temperature, and core temperature of a battery and is computationally efficient. The electrical and the thermal models are parameterized by pulse-relaxation and drive-cycle tests separately, where the electrical parameters are identified as dependent on temperature, SOC and current direction.
https://deepblue.lib.umich.edu/handle/2027.42/97341
Our group actively contributes to the battery modeling software "PyBaMM" with new models and functionality