Advanced Computer-Aided Battery Engineering Consortium

DOE’s Computer Aided Engineering for Electric-Drive Vehicle Batteries (CAEBAT) Program seeks to accelerate the battery research, development and design processes by developing and coupling computational simulation models of battery physics. First generation CAEBAT tools focused on coupling particle- and electrode-scale electrochemistry with 3D cell- and pack-scale electron and heat transport. NREL’s multiscale multidimensional (MSMD) model efficiently captured these physics at the cell scale. Commercial software companies adopted and validated similar approaches serving industry’s needs for 3D cell, pack and thermal management design tools.The Advanced Computer Aided Battery Engineering Consortium was formed in response to DOE”s 2015 CAEBAT lab call, and consists of a partnership between NREL, Argonne National Laboratory (ANL), Sandia National Laboratories (SNL) and Purdue University conducting complementary modeling/experimental studiesin battery simulation R&D. Achievements in FY2017 and FY2018 included development of parameter identification algorithms for electrochemical models, an open-source electrode microstructure library, a microstructure-scale 3D electrochemical model, characterization tools for predicting electrode tortuosity, and 3D mechanical/ electrochemical/thermal (MECT) simulation models that describe the physiochemical processes encountered by batteries in the event of vehicle crash. In FY2019, the team worked in two major thrust areas: (1) to further develop and validate MECT based on evaluation and feedback from the USCAR industry working group, and (2) to extend the electrochemical models to represent emerging battery chemistries and align future modeling projects with material-scale R&D.SNL experimental studies are further described in separate sections of this report. The project has resulted in22 archival publications, an open-source electrode microstructure library and an open-source plug-in for the FEniCS finite -element software used for 3D microstructure simulation. An open-source microstructure characterization toolbox will be released in FY2020.