Engineering topics
Publications and source records attributed to Pesaran, Ahmad A.
Five minute PowerPoint presentation giving an overview of Battery R&D activities at NREL for an audience of attended at the 2019 NAATBatt International Annual Meeting & Conference in Litchfield Park, Arizona.
This paper, presented at the 35th Annual International Battery Seminar and Exhibit 2018, discusses thermal propagation related to safety testing and simulation of lithium ion batteries.
CAE models efficiently address 3D large battery performance and safety design. Further work is needed to increase electrochemical model fidelity, reduce expense of parameter ID, and integrate degradation mechanisms, enabling optimization of designs for lifetime. Microstructure models provide deeper insight into predict effective properties of macro-homogeneous models and heterogeneous utilization of electrode degradation. Enabling 10-minute fast charge of BEV200+ requires electrolytes with enhanced transport and low electrode tortuosity.
This paper was presented at the Battery Engineering for Automotive Applications 2018 conference in San Diego, California.
NREL's Energy Storage team is exploring the effect of mechanical crush of lithium ion cells on their thermal and electrical safety. PHEV cells, fresh as well as ones aged over 8 months under different temperatures, voltage windows, and charging rates, were subjected to destructive physical analysis. Constitutive relationship and failure criteria were developed for the electrodes, separator as well as packaging material. The mechanical models capture well, the various modes of failure across different cell components. Cell level validation is being conducted by Sandia National Laboratories.
This presentation describes research work led by NREL with team members from Argonne National Laboratory and Texas A&M University in microstructure analysis, modeling and validation under DOE's Computer-Aided Engineering of Batteries (CAEBAT) program. The goal of the project is to close the gaps between CAEBAT models and materials research by creating predictive models that can be used for electrode design.
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